This paper reports recent field test results of a polarimetric hyperspectral imaging prototype sensor using a noncollinear acousto-optic tunable filter as a wavelength sorter and a polarizing beam splitter. The objective of this work is to evaluate the AOTF-PHI technology for a variety of remote sensing applications.
For a dispersive hyperspectral imaging sensor, frames are continuously being generated with spatially continuous rows of differing spectral wavelengths. As the sensor advances in the direction of travel, a hyperspectral data cube can be constructed from adjacent frames. A hyperspectral sensor residing on a satellite would require either an extremely large bandwidth for the downlink or onboard data compression to
Scott D. Briles
Hyperspectral imaging sensors greatly expand the potential of remote sensing to assess, map, and monitor marine coastal zones. Each pixel in a hyperspectral image contains an entire spectrum of information. As a result, hyperspectral image data can be processed in two very different ways: by image classification techniques, to produce mapped outputs of features in the image on a regional scale; and by use of spectral analysis of the spectral data embedded within each pixel of the image. The latter is particularly useful in marine coastal zones because of the spectral complexity of suspended as well as benthic features found in these environments. Spectral-based analysis of hyperspectral (AVIRIS) imagery was carried out to investigate a marine coastal zone of South Florida, USA. Florida Bay is a phytoplankton-rich estuary characterized by taxonomically distinct phytoplankton assemblages and extensive seagrass beds. End-member spectra were extracted from AVIRIS image data corresponding to ground-truth sample stations and well-known field sites. Spectral libraries were constructed from the AVIRIS end-member spectra and used to classify images using the Spectral Angle Mapper (SAM) algorithm, a spectral-based approach that compares the spectrum, in each pixel of an image with each spectrum in a spectral library. Using this approach different phytoplankton assemblages containing diatoms, cyanobacteria, and green microalgae, as well as benthic community (seagrasses), were mapped.
Richardson, Laurie L.
The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these test sites are not always successful due to weather and funding availability. Therefore, RSG has also automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor, This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral a imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (M0DIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of M0DlS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most brands as well as similar agreement between results that employ the different MODIS sensors as a reference.
McCorkel, Joel; Kurt, Thome; Leisso, Nathan; Anderson, Nikolaus; Czapla-Myers, Jeff
A single hyperspectral imaging sensor can produce frames with spatially-continuous rows of differing, but adjacent, spectral wavelength. If the frame sample-rate of the sensor is such that subsequent hyperspectral frames are spatially shifted by one row, then the sensor can be thought of as a parallel (in wavelength) push-broom sensor. An examination of data compression techniques for such a sensor
Scott D. Briles
Sensor simulators can be used in forecasting the imaging quality of a new hyperspectral imaging spectrometer, and generating simulated data for the development and validation of the data processing algorithms. This paper presents a novel digital sensor simulator for the pushbroom Offner hyperspectral imaging spectrometer, which is widely used in the hyperspectral remote sensing. Based on the imaging process, the sensor simulator consists of a spatial response module, a spectral response module, and a radiometric response module. In order to enhance the simulation accuracy, spatial interpolation-resampling, which is implemented before the spatial degradation, is developed to compromise the direction error and the extra aliasing effect. Instead of using the spectral response function (SRF), the dispersive imaging characteristics of the Offner convex grating optical system is accurately modeled by its configuration parameters. The non-uniformity characteristics, such as keystone and smile effects, are simulated in the corresponding modules. In this work, the spatial, spectral and radiometric calibration processes are simulated to provide the parameters of modulation transfer function (MTF), SRF and radiometric calibration parameters of the sensor simulator. Some uncertainty factors (the stability, band width of the monochromator for the spectral calibration, and the integrating sphere uncertainty for the radiometric calibration) are considered in the simulation of the calibration process. With the calibration parameters, several experiments were designed to validate the spatial, spectral and radiometric response of the sensor simulator, respectively. The experiment results indicate that the sensor simulator is valid. PMID:25615727
Tao, Dongxing; Jia, Guorui; Yuan, Yan; Zhao, Huijie
A miniaturized hyperspectral imager is enabled with image sensor integrated with dispersing elements in a very compact form factor, removing the need for expensive, moving, bulky and complex optics that have been used in conventional hyperspectral imagers for decades. The result is a handheld spectral imager that can be installed on miniature UAV drones or conveyor belts in production lines. Eventually, small handhelds can be adapted for use in outpatient medical clinics for point-of-care diagnostics and other in-field applications.
Wu, Huawen; Haibach, Frederick G.; Bergles, Eric; Qian, Jack; Zhang, Charlie; Yang, William
VTT Technical Research Centre of Finland has developed a new low cost hand-held staring hyperspectral imager for applications previously blocked by high cost of the instrumentation. The system is compatible with standard video and microscope lenses. The instrument can record 2D spatial images at several wavelength bands simultaneously. The concept of the hyperspectral imager has been published in SPIE Proc. 7474. The prototype fits in an envelope of 100 mm x 60 mm x 40 mm and its weight is ca. 300 g. The benefits of the new device compared to Acousto-Optic Tunable filter (AOTF) or Liquid Crystal Tunable Filter (LCTF) devices are small size and weight, speed of wavelength tuning, high optical throughput, independence of polarization state of incoming light and capability to record three wavelengths simultaneously. The operational wavelength range with Silicon-based CCD or CMOS sensors is 200 - 1100 nm and spectral resolution is 2 - 10 nm @ FWHM. Similar IR imagers can be built using InGaAs, InSb or MCT imaging sensors. The spatial resolution of the prototype is 480 x 750 pixels. It contains control system and memory for the image data acquisition. It operates either autonomously recording hyperspectral data cubes continuously or controlled by a laptop computer. The prototype was configured as a hyperspectral microscope for the spectral range 400 - 700 nm. The design of the hyperspectral imager, characterization results and sample measurement results are presented.
Saari, Heikki; Aallos, Ville-Veikko; Holmlund, Christer; Malinen, Jouko; Mäkynen, Jussi
Many types of hyperspectral image processing can benefit from knowledge of noise levels in the data, which can be derived from sensor physics. Surprisingly, such information is rarely provided or exploited. Usually, the image data are represented as radiance values, but this representation can lead to suboptimal results, for example in spectral difference metrics. Also, radiance data do not provide an appropriate baseline for calculation of image compression ratios. This paper defines two alternative representations of hyperspectral image data, aiming to make sensor noise accessible to image processing. A "corrected raw data" representation is proportional to the photoelectron count and can be processed like radiance data, while also offering simpler estimation of noise and somewhat more compact storage. A variance-stabilized representation is obtained by square-root transformation of the photodetector signal to make the noise signal-independent and constant across all bands while also reducing data volume by almost a factor 2. Then the data size is comparable to the fundamental information capacity of the sensor, giving a more appropriate measure of uncompressed data size. It is noted that the variance-stabilized representation has parallels in other fields of imaging. The alternative data representations provide an opportunity to reformulate hyperspectral processing algorithms to take actual sensor noise into account. PMID:21747455
A single hyperspectral imaging sensor can produce frames with spatially-continuous rows of differing, but adjacent, spectral wavelength. If the frame sample-rate of the sensor is such that subsequent hyperspectral frames are spatially shifted by one row, then the sensor can be thought of as a parallel (in wavelength) push-broom sensor. An examination of data compression techniques for such a sensor is presented. The compression techniques are intended to be implemented onboard a space-based platform and to have implementation speeds that match the date rate of the sensor. Data partitions examined extend from individually operating on a single hyperspectral frame to operating on a data cube comprising the two spatial axes and the spectral axis. Compression algorithms investigated utilize JPEG-based image compression, wavelet-based compression and differential pulse code modulation. Algorithm performance is quantitatively presented in terms of root-mean-squared error and root-mean-squared correlation coefficient error. Implementation issues are considered in algorithm development.
Emerging applications in Defense and Security require sensors with state-of-the-art sensitivity and capabilities. Among these sensors, the imaging spectrometer is an instrument yielding a large amount of rich information about the measured scene. Standoff detection, identification and quantification of chemicals in the gaseous state is one important application. Analysis of the surface emissivity as a means to classify ground properties and usage is another one. Imaging spectrometers have unmatched capabilities to meet the requirements of these applications. Telops has developed the FIRST, a LWIR hyperspectral imager. The FIRST is based on the Fourier Transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. The FIRST, a man portable sensor, provides datacubes of up to 320x256 pixels at 0.35mrad spatial resolution over the 8-12 ?m spectral range at spectral resolutions of up to 0.25cm-1. The FIRST has been used in several field campaigns, including the demonstration of standoff chemical agent detection [http://dx.doi.org/10.1117/12.795119.1]. More recently, an airborne system integrating the FIRST has been developed to provide airborne hyperspectral measurement capabilities. The airborne system and its capabilities are presented in this paper. The FIRST sensor modularity enables operation in various configurations such as tripod-mounted and airborne. In the airborne configuration, the FIRST can be operated in push-broom mode, or in staring mode with image motion compensation. This paper focuses on the airborne operation of the FIRST sensor.
Allard, Jean-Pierre; Chamberland, Martin; Farley, Vincent; Marcotte, Frédérick; Rolland, Matthias; Vallières, Alexandre; Villemaire, André
Emerging applications in Defense and Security require sensors with state-of-the-art sensitivity and capabilities. Among these sensors, the imaging spectrometer is an instrument yielding a large amount of rich information about the measured scene. Standoff detection, identification and quantification of chemicals in the gaseous state is one important application. Analysis of the surface emissivity as a means to classify ground properties and usage is another one. Imaging spectrometers have unmatched capabilities to meet the requirements of these applications. Telops has developed the FIRST, a LWIR hyperspectral imager. The FIRST is based on the Fourier Transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. The FIRST, a man portable sensor, provides datacubes of up to 320×256 pixels at 0.35mrad spatial resolution over the 8-12 ?m spectral range at spectral resolutions of up to 0.25cm-1. The FIRST has been used in several field campaigns, including the demonstration of standoff chemical agent detection [http://dx.doi.org/10.1117/12.788027.1]. More recently, an airborne system integrating the FIRST has been developed to provide airborne hyperspectral measurement capabilities. The airborne system and its capabilities are presented in this paper. The FIRST sensor modularity enables operation in various configurations such as tripod-mounted and airborne. In the airborne configuration, the FIRST can be operated in push-broom mode, or in staring mode with image motion compensation. This paper focuses on the airborne operation of the FIRST sensor.
Allard, Jean-Pierre; Chamberland, Martin; Farley, Vincent; Marcotte, Frédérick; Rolland, Matthias; Vallières, Alexandre; Villemaire, André
At present, hyperspectral images are mainly obtained with airborne sensors that are subject to turbulences while the spectrometer is acquiring the data. Therefore, geometric corrections are required to produce spatially correct images for visual interpretation and change detection analysis. This paper analyzes the data acquisition process of airborne sensors. The main objective is to propose a new data format called Diffused Matrix Format (DMF) adapted to the sensor's characteristics including its spectral and spatial information. The second objective is to compare the accuracy of the quantitative maps derived by using the DMF data structure with those obtained from raster images based on traditional data structures. Results show that DMF processing is more accurate and straightforward than conventional image processing of remotely sensed data with the advantage that the DMF file structure requires less storage space than other data formats. In addition the data processing time does not increase when DMF is used. PMID:22399919
Martínez, Pablo; Cristo, Alejandro; Koch, Magaly; Pérez, Rosa Mª.; Schmid, Thomas; Hernández, Luz M.
Hyperspectral image processing refers to the use of computer algorithms to extract, store and manipulate both spatial and spectral information contained in hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. A typical hyperspectral image processing work...
In 1983, NASA started developing hyperspectral systems to image in the ultraviolet and infrared wavelengths. In 2001, the first on-orbit hyperspectral imager, Hyperion, was launched aboard the Earth Observing-1 spacecraft. Based on the hyperspectral imaging sensors used in Earth observation satellites, Stennis Space Center engineers and Institute for Technology Development researchers collaborated on a new design that was smaller and used an improved scanner. Featured in Spinoff 2007, the technology is now exclusively licensed by Themis Vision Systems LLC, of Richmond, Virginia, and is widely used in medical and life sciences, defense and security, forensics, and microscopy.
In this paper we present an information sensing system which integrates sensing and processing resulting in the direct collection of data which is relevant to the application. Broadly, integrated sensing and processing (ISP) considers algorithms that are integrated with the collection of data. That is, traditional sensor development tries to come up with the "best" sensor in terms of SNR, resolution, data rates, integration time, etc. and traditional algorithm development tasks might wish to optimize probability of detection, false alarm rate, class separability, etc. For a typical Automatic Target Recognition (ATR) problem, the goal of ISP is to field algorithms which "tell" the sensor what kind of data to collect next and the sensor alters its parameters to collect the "best" information in order that the algorithm performs optimally. We demonstrate an ISP system which utilizes a near Infrared (NIR) Hadamard multiplexing imaging sensor. This prototype sensor incorporates a digital mirror array (DMA) device in order to realize a Hadamard multiplexed imaging system. Specific Hadamard codes can be sent to the sensor to realize inner products of the underlying scene rather than the scene itself. The developed ISP algorithm uses an ATR metric to send codes to the sensor in order to collect only the information relevant to the ATR problem. The result is a multiple resolution hyperspectral cube with full resolution where targets are present and less than full resolution where there are no targets. Essentially, this is compressed sensing.
Muise, Robert; Mahalanobis, Abhijit
Hyperspectral image consist of a set of contiguous images bands collected by a hyperspectral sensor. The large amount of data of hyperspectral images emphasizes the importance of efficient compression for storage and transmission. This paper proposes the simplified version of the three dimensional Set Partitioning Embedded bloCK (3D SPECK) algorithm for lossy compression of hyperspectral image. A three dimensional discrete
Ruzelita Ngadiran; Said Boussakta; Ahmed Bouridane; Bayan Syarif
Recognizing, online, cops and weeds enables to reduce the use of chemicals in agriculture. First, a sensor and classifier is proposed to measure and classify, online, the plant reflectance. However, as plant reflectance varies with unknown field dependent plant stress factors, the classifier must be trained on each field separately in order to recognize crop and weeds accurately on that field. Collecting the samples manually requires user-knowledge and time and is therefore economically not feasible. The posed tree-based cluster algorithm enables to automatically collect and label the necessary set of training samples for crops that are planted in rows, thus eliminating every user- interaction and user-knowledge. The classifier, trained with the automatically collected and labeled training samples, is able to recognize crop and weeds with an accuracy of almost 94 percent. This result in acceptable weed hit rates and significant herbicide reductions. Spot-spraying on the weeds only becomes economically feasible.
Feyaerts, Filip; Pollet, P.; Van Gool, Luc J.; Wambacq, Patrick
Infrared hyperspectral imaging polarimeter using birefringent prisms Julia Craven-Jones,* Michael W hyperspectral imaging polarimeter (IHIP) is introduced. The sensor includes a pair of sapphire Wollaston prisms and several high-order retarders to form an imaging Fourier transform spectropolarimeter. The Wollaston prisms
Dereniak, Eustace L.
Hyperspectral images gathered by satellites or aerial means provide a vast amount of data for geophysicists. A few applications include the exploration of minerals, research of land pollution, and military surveillance. NASA and other agencies are producing gigabytes of hyperspectral images which need to be transmitted and stored daily. As these images require high compression rates and preservation of data
Stephanie Wright; Jason Ashbach
Simulation of generic pushbroom satellite hyperspectral sensors have been performed to evaluate the potential performance and validation techniques for satellite systems such as COIS(NEMO), Warfighter-1(OrbView-4) and Hyperion(EO-1). The simulations start with a generation of synthetic scenes from material maps of studied terrain. Scene-reflected radiance is corrected for atmospheric effects and convolved with sensor spectral response using MODTRAN 4 radiance and transmissions calculations. Scene images are further convolved with point spread functions derived from Optical Transfer Functions (OTF's) of the sensor system. Photon noise and etectorr/electronics noise are added to the simulated images, which are also finally quantized to the sensor bit resolution. Studied scenes include bridges and straight roads used for evaluation of sensor spatial resolution, as well as fields of minerals, vegetation and manmade materials used for evaluation of sensor radiometric response and sensitivity. The scenes are simulated with various seasons and weather conditions. Signal-to-noise ratios and expected performance are estimated for typical satellite system specifications and are discussed for all the scenes.
Zanoni, Vicki; Stanley, Tom; Blonski, Slawomir; Cao, Changyong; Gasser, Jerry; Ryan, Robert
The hyperspectral imaging technology is one of the most important focuses of the remote sensing domain. Research on hyperspectral image compression method has important practical significance. Compared with other traditional remote sensors' data, hyperspectral images include both spatial and spectral redundancies. Most popular image coding algorithms attempt to transform the image data so that the transformed coefficients are largely uncorrelated.
Wenjie Wang; Zhongming Zhao; Haiqing Zhu
A hyperspectral imaging system of high spectral and spatial resolution that incorporates several innovative features has been developed to incorporate a focal plane scanner (U.S. Patent 6,166,373). This feature enables the system to be used for both airborne/spaceborne and laboratory hyperspectral i...
Two major issues encountered in unsupervised hyperspectral image classification are (1) how to determine the number of spectral classes in the image and (2) how to find training samples that well represent each of spectral classes without prior knowledge. A recently developed concept, Virtual dimensionality (VD) is used to estimate the number of spectral classes of interest in the image data. This paper proposes an effective algorithm to generate an appropriate training set via a recently developed Prioritized Independent Component Analysis (PICA). Two sets of hyperspectral data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Cuprite data and HYperspectral Digital Image Collection Experiment (HYDICE) data are used for experiments and performance analysis for the proposed method.
Jiao, Xiaoli; Chang, Chein-I.
A long-wave infrared hyperspectral sensor device employs a combination of an interferometer with an uncooled microbolometer array camera to produce hyperspectral images without the use of bulky, power-hungry motorized components, making it suitable for UAV vehicles, small mobile platforms, or in extraterrestrial environments. The sensor device can provide signal-to-noise ratios near 200 for ambient temperature scenes with 33 wavenumber resolution at a frame rate of 50 Hz, with higher results indicated by ongoing component improvements.
Lucey, Paul G. (Inventor)
Fast and small foot print lossless image compressors aiming at hyper-spectral sensor for the earth observation satellite have been developed. Since more than one hundred channels are required for hyper-spectral sensors on optical observation satellites, fast compression algorithm with small foot print implementation is essential for reducing encoder size and weight resulting in realizing light-weight and small-size sensor system. The image compression method should have low complexity in order to reduce size and weight of the sensor signal processing unit, power consumption and fabrication cost. Coding efficiency and compression speed enables enlargement of the capacity of signal compression channels, which resulted in reducing signal compression channels onboard by multiplexing sensor signal channels into reduced number of compression channels. The employed method is based on FELICS1, which is hierarchical predictive coding method with resolution scaling. To improve FELICS's performance of image decorrelation and entropy coding, we applied two-dimensional interpolation prediction and adaptive Golomb-Rice coding, which enables small footprint. It supports progressive decompression using resolution scaling, whilst still delivering superior performance as measured by speed and complexity. The small footprint circuitry is embedded into the hyper-spectral sensor data formatter. In consequence, lossless compression function has been added without additional size and weight.
Hihara, Hiroki; Yoshida, Jun; Ishida, Juro; Takada, Jun; Senda, Yuzo; Suzuki, Makoto; Seki, Taeko; Ichikawa, Satoshi; Ohgi, Nagamitsu
Programmable hyperspectral imaging mapper (PHIMAP) is conceptual generic advanced spectral imaging system that scans swath along ground track from airplane or spacecraft in orbit. Features include spectrally agile filters and processing on focal-plane array. Provides both high spatial resolution and large number of spectral channels. Can be programmed to trade spectral resolution andspectral coverage flexibly against signal-to-noise ratio to optimize utility of image data from scenes of spectrally and spatially varying brightness.
Cutts, James A.
Parallel Implementation of Hyperspectral Image Processing Algorithms Antonio Plaza, David Valencia hyperspectral imaging applications, including automatic target recognition for homeland defense and security the growing interest in hyperspectral imaging research, only a few efforts devoted to designing
Plaza, Antonio J.
Hyperspectral images gathered by satellites or aerial means provide a vast amount of data for geophysicists. A few applications include the exploration of minerals, research of land pollution, and military surveillance. NASA and other agencies are producing gigabytes of hyperspectral images which need to be transmitted and stored daily. As these images require high compression rates and preservation of data integrity, we are presented with an intriguing compression problem. In our research we investigate two compression algorithms: a near-lossless technique based on minimizing maximum absolute distortion (MAD) and a lossy based algorithm which minimizes mean squared error (MSE). Near-lossless algorithms provide high compression rates and a uniform distribution of error. Whereas MSE based algorithms yield high compression rates but a non-uniform distribution of error. Our goal is to determine which algorithm yields high compression rates and minimal data loss without modifying post processing of hyperspectral data. In order to compare these two compression algorithms and determine their effect on post processing we used ENVI's image processing tools. We classified the decompressed images for each algorithm and compared them to the classified original image.
Wright, Stephanie; Miguel, Agnieszka, , Dr.; Ashbach, Jason
A hyperspectral imaging system of high spectral and spatial resolution that incorporates several innovative features has been developed to incorporate a focal plane scanner (U.S. Patent 6,166,373). This feature enables the system to be used for both airborne/spaceborne and laboratory hyperspectral imaging with or without relative movement of the imaging system, and it can be used to scan a target of any size as long as the target can be imaged at the focal plane; for example, automated inspection of food items and identification of single-celled organisms. The spectral resolution of this system is greater than that of prior terrestrial multispectral imaging systems. Moreover, unlike prior high-spectral resolution airborne and spaceborne hyperspectral imaging systems, this system does not rely on relative movement of the target and the imaging system to sweep an imaging line across a scene. This compact system (see figure) consists of a front objective mounted at a translation stage with a motorized actuator, and a line-slit imaging spectrograph mounted within a rotary assembly with a rear adaptor to a charged-coupled-device (CCD) camera. Push-broom scanning is carried out by the motorized actuator which can be controlled either manually by an operator or automatically by a computer to drive the line-slit across an image at a focal plane of the front objective. To reduce the cost, the system has been designed to integrate as many as possible off-the-shelf components including the CCD camera and spectrograph. The system has achieved high spectral and spatial resolutions by using a high-quality CCD camera, spectrograph, and front objective lens. Fixtures for attachment of the system to a microscope (U.S. Patent 6,495,818 B1) make it possible to acquire multispectral images of single cells and other microscopic objects.
Mao, Chengye; Smith, David; Lanoue, Mark A.; Poole, Gavin H.; Heitschmidt, Jerry; Martinez, Luis; Windham, William A.; Lawrence, Kurt C.; Park, Bosoon
Hyperspectral imaging is a non-destructive optical analysis technique that can for instance be used to obtain information from cultural heritage objects unavailable with conventional colour or multi-spectral photography. This technique can be used to distinguish and recognize materials, to enhance the visibility of faint or obscured features, to detect signs of degradation and study the effect of environmental conditions on the object. We describe the basic concept, working principles, construction and performance of a laboratory instrument specifically developed for the analysis of historical documents. The instrument measures calibrated spectral reflectance images at 70 wavelengths ranging from 365 to 1100 nm (near-ultraviolet, visible and near-infrared). By using a wavelength tunable narrow-bandwidth light-source, the light energy used to illuminate the measured object is minimal, so that any light-induced degradation can be excluded. Basic analysis of the hyperspectral data includes a qualitative comparison of the spectral images and the extraction of quantitative data such as mean spectral reflectance curves and statistical information from user-defined regions-of-interest. More sophisticated mathematical feature extraction and classification techniques can be used to map areas on the document, where different types of ink had been applied or where one ink shows various degrees of degradation. The developed quantitative hyperspectral imager is currently in use by the Nationaal Archief (National Archives of The Netherlands) to study degradation effects of artificial samples and original documents, exposed in their permanent exhibition area or stored in their deposit rooms.
Klein, Marvin E.; Aalderink, Bernard J.; Padoan, Roberto; de Bruin, Gerrit; Steemers, Ted A.G.
Remote sensing techniques now include the use of hyperspectral infrared imaging sensors covering the mid-and- long wave regions of the spectrum. They have found use in military surveillance applications due to their capability for detection and classification of a large variety of both naturally occurring and man-made substances. The images they produce reveal the spatial distributions of spectral patterns that
Donald B. Malkoff; William R. Oliver
Here we propose scalable Three-Dimensional Set Partitioned Embedded bloCK (3D-SPECK)–an embedded, block-based, wavelet transform coding algorithm of low complexity for hyperspectral image compression. Scalable 3D-SPECK supports both SNR and resolution progressive coding. After wavelet transform, 3D-SPECK treats each subband as a coding block. To generate SNR scalable bitstream, the stream is organized so that the same indexed bit planes are
Xiaoli Tang; William A. Pearlman
A hyperspectral imaging spectrometer was breadboarded. Key innovations were use of a sapphire prism and single InSb focal plane to cover the entire spectral range, and a novel slit optic and relay optics to reduce thermal background. Operation over a spectral range of 450 - 4950 nm (approximately 3.5 spectral octaves) was demonstrated. Thermal background reduction by a factor of 8 - 10 was also demonstrated.
We describe a compressive projection algorithm and experimentally assess its performance when used with a Hyperspectral Image Projector (HIP). The HIP is being developed by NIST for system-level performance testing of hyperspectral and multispectral imagers. It projects a two-dimensional image into the unit under test (UUT), whereby each pixel can have an independently programmable arbitrary spectrum. To efficiently project a
Joseph P. Rice; David W. Allen
VTT Technical Research Centre of Finland has developed a new miniaturized staring hyperspectral imager with a weight of 350 g making the system compatible with lightweight UAS platforms. The instrument is able to record 2D spatial images at the selected wavelength bands simultaneously. The concept of the hyperspectral imager has been published in the SPIE Proc. 74741. The operational wavelength range of the imager can be tuned in the range 400 - 1100 nm and spectral resolution is in the range 5 - 10 nm @ FWHM. Presently the spatial resolution is 480 × 750 pixels but it can be increased simply by changing the image sensor. The field of view of the system is 20 × 30 degrees and ground pixel size at 100 m flying altitude is around 7.5 cm. The system contains batteries, image acquisition control system and memory for the image data. It can operate autonomously recording hyperspectral data cubes continuously or controlled by the autopilot system of the UAS. The new hyperspectral imager prototype was first tried in co-operation with the Flemish Institute for Technological Research (VITO) on their UAS helicopter. The instrument was configured for the spectral range 500 - 900 nm selected for the vegetation and natural water monitoring applications. The design of the UAS hyperspectral imager and its characterization results together with the analysis of the spectral data from first test flights will be presented.
Saari, Heikki; Aallos, Ville-Veikko; Holmlund, Christer; Mäkynen, Jussi; Delauré, Bavo; Nackaerts, Kris; Michiels, Bart
The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring...
A document discusses a hyperspectral imaging instrument package designed to be carried aboard a helicopter. It was developed to map the depths of Greenland's supraglacial lakes. The instrument is capable of telescoping to twice its original length, allowing it to be retracted with the door closed during takeoff and landing, and manually extended in mid-flight. While extended, the instrument platform provides the attached hyperspectral imager a nadir-centered and unobstructed view of the ground. Before flight, the instrument mount is retracted and securely strapped down to existing anchor points on the floor of the helicopter. When the helicopter reaches the destination lake, the door is opened and the instrument mount is manually extended. Power to the instrument package is turned on, and the data acquisition computer is commanded via a serial cable from an onboard user-operated laptop to begin data collection. After data collection is complete, the instrument package is powered down and the mount retracted, allowing the door to be closed in preparation for landing. The present design for the instrument mount consists of a three-segment telescoping cantilever to allow for a sufficient extended length to see around the landing struts and provide a nadir-centered and unobstructed field of view for the hyperspectral imager. This instrument works on the premise that water preferentially absorbs light with longer wavelengths on the red side of the visible spectrum. This property can be exploited in order to remotely determine the depths of bodies of pure freshwater. An imager flying over such a lake receives light scattered from the surface, the bulk of the water column, and from the lake bottom. The strength of absorption of longer-wavelength light depends on the depth of the water column. Through calibration with in situ measurements of the water depths, a depth-determining algorithm may be developed to determine lake depth from these spectral properties of the reflected sunlight.
Behar, Alberto E.; Cooper, Moogega; Adler, John; Jacobson, Tobias
Focal plane arrays with associated electronics and cooling are a substantial portion of the cost, complexity, size, weight, and power requirements of Long-Wave IR (LWIR) imagers. Hyperspectral LWIR imagers add significant data volume burden as they collect a high-resolution spectrum at each pixel. We report here on a LWIR Hyperspectral Sensor that applies Compressive Sensing (CS) in order to achieve benefits in these areas. The sensor applies single-pixel detection technology demonstrated by Rice University. The single-pixel approach uses a Digital Micro-mirror Device (DMD) to reflect and multiplex the light from a random assortment of pixels onto the detector. This is repeated for a number of measurements much less than the total number of scene pixels. We have extended this architecture to hyperspectral LWIR sensing by inserting a Fabry-Perot spectrometer in the optical path. This compressive hyperspectral imager collects all three dimensions on a single detection element, greatly reducing the size, weight and power requirements of the system relative to traditional approaches, while also reducing data volume. The CS architecture also supports innovative adaptive approaches to sensing, as the DMD device allows control over the selection of spatial scene pixels to be multiplexed on the detector. We are applying this advantage to the detection of plume gases, by adaptively locating and concentrating target energy. A key challenge in this system is the diffraction loss produce by the DMD in the LWIR. We report the results of testing DMD operation in the LWIR, as well as system spatial and spectral performance.
Russell, Thomas A.; McMackin, Lenore; Bridge, Bob; Baraniuk, Richard
Image multiplexing is the technique of using combination patterns to measure multiple pixels with one sensor. Hyperspectral imaging is acquiring images with full spectra at each pixel. Using a single point spectrometer and light modulation we perform multiplexed hyperspectral imaging. We compare two forms of multiplexing, namely Hadamard imaging and compressed sensing, at low resolution. We show that Hadamard imaging
L. Streeter; G. R. Burling-Claridge; M. J. Cree; R. Kunnemeyer
Passive, standoff detection of chemical, explosive and narcotic threats employing widefield, shortwave infrared (SWIR) hyperspectral imaging (HSI) continues to gain acceptance in defense and security fields. A robust and user-friendly portable platform with such capabilities increases the effectiveness of locating and identifying threats while reducing risks to personnel. In 2013 ChemImage Sensor Systems (CISS) introduced Aperio, a handheld sensor, using real-time SWIR HSI for wide area surveillance and standoff detection of explosives, chemical threats, and narcotics. That SWIR HSI system employed a liquid-crystal tunable filter for real-time automated detection and display of threats. In these proceedings, we report on a next generation device called VeroVision™, which incorporates an improved optical design that enhances detection performance at greater standoff distances with increased sensitivity and detection speed. A tripod mounted sensor head unit (SHU) with an optional motorized pan-tilt unit (PTU) is available for precision pointing and sensor stabilization. This option supports longer standoff range applications which are often seen at checkpoint vehicle inspection where speed and precision is necessary. Basic software has been extended to include advanced algorithms providing multi-target display functionality, automatic threshold determination, and an automated detection recipe capability for expanding the library as new threats emerge. In these proceedings, we report on the improvements associated with the next generation portable widefield SWIR HSI sensor, VeroVision™. Test data collected during development are presented in this report which supports the targeted applications for use of VeroVision™ for screening residue and bulk levels of explosive and drugs on vehicles and personnel at checkpoints as well as various applications for other secure areas. Additionally, we highlight a forensic application of the technology for assisting forensic investigators in locating bone remains in a cluttered background environment.
Nelson, Matthew P.; Gardner, Charles W.; Klueva, Oksana; Tomas, David
The Hyperspectral Imager-Tracker (HIT) is a technique for visualization and tracking of low-contrast, fast-moving objects. The HIT architecture is based on an innovative and only recently developed concept in imaging optics. This innovative architecture will give the Light Prescriptions Innovators (LPI) HIT the possibility of simultaneously collecting the spectral band images (hyperspectral cube), IR images, and to operate with high-light-gathering power and high magnification for multiple fast- moving objects. Adaptive Spectral Filtering algorithms will efficiently increase the contrast of low-contrast scenes. The most hazardous parts of a space mission are the first stage of a launch and the last 10 kilometers of the landing trajectory. In general, a close watch on spacecraft operation is required at distances up to 70 km. Tracking at such distances is usually associated with the use of radar, but its milliradian angular resolution translates to 100- m spatial resolution at 70-km distance. With sufficient power, radar can track a spacecraft as a whole object, but will not provide detail in the case of an accident, particularly for small debris in the onemeter range, which can only be achieved optically. It will be important to track the debris, which could disintegrate further into more debris, all the way to the ground. Such fragmentation could cause ballistic predictions, based on observations using high-resolution but narrow-field optics for only the first few seconds of the event, to be inaccurate. No optical imager architecture exists to satisfy NASA requirements. The HIT was developed for space vehicle tracking, in-flight inspection, and in the case of an accident, a detailed recording of the event. The system is a combination of five subsystems: (1) a roving fovea telescope with a wide 30 field of regard; (2) narrow, high-resolution fovea field optics; (3) a Coude optics system for telescope output beam stabilization; (4) a hyperspectral-mutispectral imaging assembly; and (5) image analysis software with effective adaptive spectral filtering algorithm for real-time contrast enhancement.
METRIC LEARNING FOR HYPERSPECTRAL IMAGE SEGMENTATION Brian D. Bue1 , David R. Thompson2 , Martha S hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and auto, Metric Learning, CRISM 1. HYPERSPECTRAL IMAGE SEGMENTATION Unsupervised hyperspectral image segmentations
HYPERSPECTRAL IMAGING: SIGNAL PROCESSING ALGORITHM DESIGN AND ANALYSIS Chein-I Chang Remote Sensing: Independent Component Analysis-Based Abundance Quantification PART V: HYPERSPECTRAL IMAGE COMPRESSION Chapter Chapter 19: Spectral/Spatial Hyperspectral Image Compression Chapter 20: Hyperspectral Information
Abstract. Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. PMID:24441941
Lu, Guolan; Fei, Baowei
Simulations of generic pushbroom satellite hyperspetral sensors have been performed to evaluate the potential performance and validation techniques for satellite systems such as COIS (NEMO), Warfighter-1 (OrbView-4), and Hyperion (EO-1). The simulaitons start with a generation of synthetic scenes form material maps of studied terrain. Scene-reflected radiance is corrected for atmospheric effects and convolved with sensor spectral response uwing MODTRAN 4 radiance and transmission calculations. Scene images are further convolved with point spread functions derived from Optical Transfer Functions (OTF's) of the sensor system. Photon noise and detector/electronics noise are added to the simulated images, which are also finally quantized to the sensor bit resolution. Studied scenes include bridges and straight roads used for evaluation of sensor spatial resolution, as well as fields of minerals, vegetation, and manmade materials used for evaluation of sensor radiometric response and sensitivity. The scenes are simulated with various seasons and weather conditions. Signal-to-noise ratos and expected performance are estimated for typica satellite system specifications and are discussed for all the scenes.
Zanoni, Vicki; Stanley, Tom; Blonski, Slawomir; Cao, Changyong; Gasser, Jerry; Ryan, Robert; Zanoni, Vicki; Stanley, Tom
A method is presented that attempts to isolate the relative magnitudes of various error sources present in common algorithms for inverting the effects of atmospheric scattering and absorption on solar irradiance and determine in what ways, if any, operational ground truth measurement systems can be employed to reduce the over-all error in retrieved reflectance factor. Error modeling and propagation methodology is developed for each link in the imaging chain, and representative values are determined for the purpose of exercising the model and observing the system behavior in response to a wide variety of inputs. Three distinct approaches to model-based atmospheric inversion are compared in a common reflectance error space, where each contributor to the overall error in retrieved reflectance is examined in relation to the others. The modeling framework also allows for performance predictions resulting from the incorporation of operational ground truth measurements. Regimes were identified in which uncertainty in water vapor and aerosols were each found to dominate error contributions to final retrieved reflectance. Cloud cover was also shown to be a significant contributor, while state-of-the-industry hyperspectral sensors were confirmed to not be error drivers. Accordingly, instruments for measuring water vapor, aerosols, and downwelled sky radiance were identified as key to improving reflectance retrieval beyond current performance by current inversion algorithms.
The hyperspectral imaging spectrometer can supply hundreds of narrow band spectral data, which has high spatial and spectral resolution, and meanwhile the amount of data becomes huge. Therefore, the efficient compression algorithms become necessary. According to the characteristics of hyperspectral images, spatial and spectral decorrelation is necessary before compression. In this paper, the characteristics of hyperspectral images are presented firstly.
Hai-ping Wai; Bao-jun Zhao; Pei-kun He
The SPIRIT system is a spectrally agile IR imaging airborne camera, with the capability to select any of the multiple filters on a frame by frame basis. The implemented solution employs advanced, but proven, technology to meet the objectives, and achieved good spatial and thermal performance in all modes. Sophisticated electronic design has results in a flexible unit, which can respond to the changing requirements of the user. Initial SPIRIT flight trials were undertaken in summer 1998 with more scheduled to continue through 1999. The sensor was installed on to DERA's TIARA research platform, a modified Tornado F2. The flight trials to date have been conducted over a variety of scenarios, collecting spectral data in up to 12 bands, of other aircraft, tanks, and fixed targets. Further ground- based trials, with the sensor mounted on a pan and tilt tracking platform, have been performed on characterized targets and against further air targets. Data from these initial trials are currently being processed to assess whether sufficient spectral information is available to discriminate between target types at militarily significant ranges. Sample hyperspectral imagery form SPIRIT and some results are presented.
Evans, Stephen C.; Hargreaves, John; Evans, Paul H.; Randall, Peter N.; Bernhardt, Mark
Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne- and satellite-based sensors. Often, ground-truth measurements at these tests sites are not always successful due to weather and funding availability. Therefore, RSG has also employed automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor. This dissertation presents a method for determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on a multispectral sensor, Moderate-resolution Imaging Spectroradiometer (MODIS), as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. A method to predict hyperspectral surface reflectance using a combination of MODIS data and spectral shape information is developed and applied for the characterization of Hyperion. Spectral shape information is based on RSG's historical in situ data for the Railroad Valley test site and spectral library data for the Libyan test site. Average atmospheric parameters, also based on historical measurements, are used in reflectance prediction and transfer to space. Results of several cross-calibration scenarios that differ in image acquisition coincidence, test site, and reference sensor are found for the characterization of Hyperion. These are compared with results from the reflectance-based approach of vicarious calibration, a well-documented method developed by the RSG that serves as a baseline for calibration performance for the cross-calibration method developed here. Cross-calibration provides results that are within 2% of those of reflectance-based results in most spectral regions. Larger disagreements exist for shorter wavelengths studied in this work as well as in spectral areas that experience absorption by the atmosphere.
ChemImage has developed a SWIR Hyperspectral Imaging (HSI) sensor which uses hyperspectral imaging for wide area surveillance and standoff detection of surface residues. Existing detection technologies often require close proximity for sensing or detecting, endangering operators and costly equipment. Furthermore, most of the existing sensors do not support autonomous, real-time, mobile platform based detection of threats. The SWIR HSI sensor provides real-time standoff detection of surface residues. The SWIR HSI sensor provides wide area surveillance and HSI capability enabled by liquid crystal tunable filter technology. Easy-to-use detection software with a simple, intuitive user interface produces automated alarms and real-time display of threat and type. The system has potential to be used for the detection of variety of threats including chemicals and illicit drug substances and allows for easy updates in the field for detection of new hazardous materials. SWIR HSI technology could be used by law enforcement for standoff screening of suspicious locations and vehicles in pursuit of illegal labs or combat engineers to support route-clearance applications- ultimately to save the lives of soldiers and civilians. In this paper, results from a SWIR HSI sensor, which include detection of various materials in bulk form, as well as residue amounts on vehicles, people and other surfaces, will be discussed.
Nelson, Matthew P.; Mangold, Paul; Gomer, Nathaniel; Klueva, Oksana; Treado, Patrick
There is a user need for increasing spatial and spectral resolution in Earth Observation (EO) optical instrumentation. Higher spectral resolution will be achieved by the introduction of spaceborne imaging spectrometers. Higher spatial resolutions of 1 - 3m will be achieved also, but at the expense of sensor redesign, higher communications bandwidth, high data processing volumes, and therefore, at the risk of time delays due to large volume data-handling bottlenecks. This paper discusses a design concept whereby the hyperspectral properties of a spaceborne imaging spectrometer can be used to increase the image spatial resolution, without such adverse cost impact.
Burke, Ian; Zwick, Harold
Hyperspectral imagery typically possesses high spectral resolution but low spatial resolution. One way to enhance the spatial resolution of a hyperspectral image is to fuse its spectral information and the spatial information of another high resolution image. In this paper, we propose a novel image fusion strategy for hyperspectral image and high spatial resolution panchromatic image, which is based on the curvelet transform. Firstly, determine a synthesized image with the specified RGB bands of the original hyperspectral images according to the optimal index factor (OIF) model. Then use the IHS transform to extract the intensity component of the synthesized image. After that, the histogram matching is performed between the intensity component and the panchromatic image. Thirdly, the curvelet transform is applied to decompose the two source images (the intensity component and the panchromatic image) in different scales and directions. Different fusion strategies are applied to coefficients in various scales and directions. Finally, the fused image is achieved by the inverse IHS transform. The experimental result shows that the proposed method has a superior performance. Comparing with the traditional methods such as the PCA transform, wavelet-based or pyramid-based methods and the multi-resolution fusion methods (shearlet or contourlet decomposition), the fused image achieves the highest entropy index and average gradient value. While providing a better human visual quality, a good correlation coefficient index indicates that the fused image keeps good spectral information. Both visual quality and objective evaluation criteria demonstrate that this method can well preserve the spatial quality and the spectral characteristics.
Wang, Sha; Feng, Hua-jun; Xu, Zhi-hai; Li, Qi; Chen, Yue-ting
In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics - originally introduced by the physicist H. Haken - is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM), Spectral Information Divergence (SID), Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM). The outcomes are promising and often outperform state-of-the-art classification methodologies.
Müller, R.; Cerra, D.; Reinartz, P.
Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has the spatial resolution of the multispectral image and spectral resolution of the hyperspectral image, by fusion of hyperspectral and multispectral image. The proposed methodology also addresses the band remapping problem, which arises due to different regions of spectral coverage by multispectral and hyperspectral images. Therefore we apply algorithms to restore the spatial information of the hyperspectral image by fusing hyperspectral bands with only those bands which come under each multispectral band range. The proposed methodology is applied over Henry Island, of the Sunderban eco-geographic province. The data is collected by the Hyperion hyperspectral sensor and LISS IV multispectral sensor.
Chakravortty, S.; Subramaniam, P.
We describe a compressive projection algorithm and experimentally assess its performance when used with a Hyperspectral Image Projector (HIP). The HIP is being developed by NIST for system-level performance testing of hyperspectral and multispectral imagers. It projects a two-dimensional image into the unit under test (UUT), whereby each pixel can have an independently programmable arbitrary spectrum. To efficiently project a single frame of dynamic realistic hyperspectral imagery through the collimator into the UUT, a compression algorithm has been developed whereby the series of abundance images and corresponding endmember spectra that comprise the image cube of that frame are first computed using an automated endmember-finding algorithm such as the Sequential Maximum Angle Convex Cone (SMACC) endmember model. Then these endmember spectra are projected sequentially on the HIP spectral engine in sync with the projection of the abundance images on the HIP spatial engine, during the singleframe exposure time of the UUT. The integrated spatial image captured by the UUT is the endmember-weighted sum of the abundance images, which results in the formation of a datacube for that frame. Compressive projection enables a much smaller set of broadband spectra to be projected than monochromatic projection, and thus utilizes the inherent multiplex advantage of the HIP spectral engine. As a result, radiometric brightness and projection frame rate are enhanced. In this paper, we use a visible breadboard HIP to experimentally assess the compressive projection algorithm performance.
Rice, Joseph P.; Allen, David W.
of hyperspectral image compression algorithms on specialized hardware devices are currently being investigatedGPU Implementation of JPEG2000 for Hyperspectral Image Compression Milosz Ciznickia, Krzysztof has been successfully used in the context of hyperspectral image compression, either in lossless
Plaza, Antonio J.
- vised and supervised classification, spectral unmixing and compression of hyperspectral image data. Most of hyperspectral imaging mixture analysis, or data compression . For instance, several computational7 Parallel Spatial-Spectral Processing of Hyperspectral Images Antonio J. Plaza Department
Plaza, Antonio J.
The Civil Air Patrol (CAP) is procuring Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) systems to increase their search-and-rescue mission capability. These systems are being installed on a fleet of Gippsland GA-8 aircraft, and will position CAP to gain realworld mission experience with the application of hyperspectral sensor and processing technology to search and rescue. The ARCHER system design, data processing, and operational concept leverage several years of investment in hyperspectral technology research and airborne system demonstration programs by the Naval Research Laboratory (NRL) and Air Force Research Laboratory (AFRL). Each ARCHER system consists of a NovaSol-designed, pushbroom, visible/near-infrared (VNIR) hyperspectral imaging (HSI) sensor, a co-boresighted visible panchromatic high-resolution imaging (HRI) sensor, and a CMIGITS-III GPS/INS unit in an integrated sensor assembly mounted inside the GA-8 cabin. ARCHER incorporates an on-board data processing system developed by Space Computer Corporation (SCC) to perform numerous real-time processing functions including data acquisition and recording, raw data correction, target detection, cueing and chipping, precision image geo-registration, and display and dissemination of image products and target cue information. A ground processing station is provided for post-flight data playback and analysis. This paper describes the requirements and architecture of the ARCHER system, including design, components, software, interfaces, and displays. Key sensor performance characteristics and real-time data processing features are discussed in detail. The use of the system for detecting and geo-locating ground targets in real-time is demonstrated using test data collected in Southern California in the fall of 2004.
Stevenson, Brian; O'Connor, Rory; Kendall, William; Stocker, Alan; Schaff, William; Holasek, Rick; Even, Detlev; Alexa, Drew; Salvador, John; Eismann, Michael; Mack, Robert; Kee, Pat; Harris, Steve; Karch, Barry; Kershenstein, John
The gap between airborne imaging spectroscopy and traditional multi spectral satellite sensors is decreasing thanks to a new generation of satellite sensors of which CHRIS mounted on the small and low-cost PROBA satellite is the prototype. Although image acquisition and analysis are still in a test phase, the high spatial and spectral resolution and pointability have proved their potential. Because
Barbara Van Mol; Kevin Ruddick
The hyperspectral imaging spectrometer can supply hundreds of narrow band spectral data, which has high spatial and spectral resolution, and meanwhile the amount of data becomes huge. Therefore, the efficient compression algorithms become necessary. According to the characteristics of hyperspectral images, spatial and spectral decorrelation is necessary before compression. In this paper, the characteristics of hyperspectral images are presented firstly. Secondly, the research on hyperspectral image decorrelation is summarized. Techniques based on prediction, techniques based on vector quantization, and techniques based on transform coding are presented here. Finally, the future development is referred.
Wai, Hai-ping; Zhao, Bao-jun; He, Pei-kun
Image quality assessment is an essential value judgement approach for many applications. Multi & hyper spectral imaging has more judging essentials than grey scale or RGB imaging and its image quality assessment job has to cover up all-around evaluating factors. This paper presents an integrating spectral imaging quality assessment project, in which spectral-based, radiometric-based and spatial-based statistical behavior for three hyperspectral imagers are jointly executed. Spectral response function is worked out based on discrete illumination images and its spectral performance is deduced according to its FWHM and spectral excursion value. Radiometric response ability of different spectral channel under both on-ground and airborne imaging condition is judged by SNR computing based upon local RMS extraction and statistics method. Spatial response evaluation of the spectral imaging instrument is worked out by MTF computing with slanted edge analysis method. Reported pioneering systemic work in hyperspectral imaging quality assessment is carried out with the help of several domestic dominating work units, which not only has significance in the development of on-ground and in-orbit instrument performance evaluation technique but also takes on reference value for index demonstration and design optimization for instrument development.
Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Shen, Weimin
Remote sensing techniques now include the use of hyperspectral infrared imaging sensors covering the mid-and- long wave regions of the spectrum. They have found use in military surveillance applications due to their capability for detection and classification of a large variety of both naturally occurring and man-made substances. The images they produce reveal the spatial distributions of spectral patterns that reflect differences in material temperature, texture, and composition. A program is proposed for demonstrating proof-of-concept in using a portable sensor of this type for crime scene investigations. It is anticipated to be useful in discovering and documenting the affects of trauma and/or naturally occurring illnesses, as well as detecting blood spills, tire patterns, toxic chemicals, skin injection sites, blunt traumas to the body, fluid accumulations, congenital biochemical defects, and a host of other conditions and diseases. This approach can significantly enhance capabilities for determining the circumstances of death. Potential users include law enforcement organizations (police, FBI, CIA), medical examiners, hospitals/emergency rooms, and medical laboratories. Many of the image analysis algorithms already in place for hyperspectral remote sensing and crime scene investigations can be applied to the interpretation of data obtained in this program.
Malkoff, Donald B.; Oliver, William R.
In many hyperspectral applications it is beneficial to produce 2D spatial images with a single exposure at a few selected wavelength bands instead of 1D spatial and all spectral band images like in push-broom instruments. VTT has developed a new concept based on the Piezo actuated Fabry-Perot Interferometer to enable recording of 2D spatial images at the selected wavelength bands simultaneously. The sensor size is compatible with light weight UAV platforms. In our spectrometer the multiple orders of the Fabry-Perot Interferometer are used at the same time matched to the sensitivities of a multispectral RGB-type image sensor channels. We have built prototypes of the new spectrograph fitting inside of a 40 mm x 40 mm x 20 mm envelope and with a mass less than 50 g. The operational wavelength range of built prototypes can be tuned in the range 400 - 1100 nm and the spectral resolution is in the range 5 - 10 nm @ FWHM. Presently the spatial resolution is 480 x 750 pixels but it can be increased simply by changing the image sensor. The hyperspectral imager records simultaneously a 2D image of the scenery at three narrow wavelength bands determined by the selected three orders of the Fabry-Perot Interferometer which depend on the air gap between the mirrors of the Fabry-Perot Cavity. The new sensor can be applied on UAV, aircraft, and other platforms requiring small volume, mass and power consumption. The new low cost hyperspectral imager can be used also in many industrial and medical applications.
Saari, Heikki; Aallos, Ville-Veikko; Akujärvi, Altti; Antila, Tapani; Holmlund, Christer; Kantojärvi, Uula; Mäkynen, Jussi; Ollila, Jyrki
The use of hyperspectral data to determine the abundance of constituents in a certain portion of the Earth's surface relies on the capability of imaging spectrometers to provide a large amount of information at each pixel of a certain scene. Today, hyperspectral imaging sensors are capable of generating unprecedented volumes of radiometric data. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), for example, routinely produces image cubes with 224 spectral bands. This undoubtedly opens a wide range of new possibilities, but the analysis of such a massive amount of information is not an easy task. In fact, most of the existing algorithms devoted to analyzing multispectral images are not applicable in the hyperspectral domain, because of the size and high dimensionality of the images. The application of neural networks to perform unsupervised classification of hyperspectral data has been tested by several authors and also by us in some previous work. We have also focused on analyzing the intrinsic capability of neural networks to parallelize the whole hyperspectral unmixing process. The results shown in this work indicate that neural network models are able to find clusters of closely related hyperspectral signatures, and thus can be used as a powerful tool to achieve the desired classification. The present work discusses the possibility of using a Self Organizing neural network to perform unsupervised classification of hyperspectral images. In sections 3 and 4, the topology of the proposed neural network and the training algorithm are respectively described. Section 5 provides the results we have obtained after applying the proposed methodology to real hyperspectral data, described in section 2. Different parameters in the learning stage have been modified in order to obtain a detailed description of their influence on the final results. Finally, in section 6 we provide the conclusions at which we have arrived.
Martinez, P.; Gualtieri, J. A.; Aguilar, P. L.; Perez, R. M.; Linaje, M.; Preciado, J. C.; Plaza, A.
Information on imaging spectrometry is given in the forms of outlines, graphs, and charts. Topics covered include impacts on science users, program objectives, expert systems for imaging spectrometry, and imaging spectrometer data analysis methods.
In this paper, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation. After classification the assessment of similarity between original image and classified image is achieved by measurements of image quality parameters. Experiments were carried out on four different types of hyperspectral images. Aerial and spaceborne hyperspectral images with different spectral and geometric resolutions were considered for quality metrics evaluation. Principal Component Analysis (PCA) has been applied to reduce the dimensionality of hyperspectral data. PCA was ultimately used for reducing the number of effective variables resulting in reduced complexity in processing. In case of ordinary images a human viewer plays an important role in quality evaluation. Hyperspectral data are generally processed by automatic algorithms and hence cannot be viewed directly by human viewers. Therefore evaluating quality of classified image becomes even more significant. An elaborate comparison is made between k-means clustering and segmentation for all the images by taking Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Maximum Squared Error, ratio of squared norms called L2RAT and Entropy. First four parameters are calculated by comparing the quality of original hyperspectral image and classified image. Entropy is a measure of uncertainty or randomness which is calculated for classified image. Proposed methodology can be used for assessing the performance of any hyperspectral image classification techniques.
Singh, A. K.; Kumar, H. V.; Kadambi, G. R.; Kishore, J. K.; Shuttleworth, J.; Manikandan, J.
Near infrared reflectance (NIR) spectroscopy is well established in the food industry for rapid compositional analysis of bulk samples. NIR hyperspectral imaging provides new opportunities to measure the spatial distribution of components such as moisture and fat, and to identify and measure specific regions of composite samples. An NIR hyperspectral imaging system has been constructed for food research applications, incorporating
Martin B. Whitworth; Samuel J. Millar; Astor Chau
Machine vision methods for quality and defect evaluation of tomatoes have been studied for online sorting and robotic harvesting applications. We investigated the use of a hyperspectral imaging system for quality evaluation and defect detection for tomatoes. Hyperspectral reflectance images were a...
This paper presents a new method for detecting poultry skin tumors based on serial feature fusion in hyperspectral images. First, some transform methods, including principal component analysis, discrete wavelet transform and band ratio method, are used to generate largely independent datasets in the hyperspectral fluorescence images. Then, the kernel discriminant analysis is utilized to extract features from each represented dataset
Chengzhe Xu; Intaek Kim; Seong G. Kong
The huge data volume of hyperspectral image challenges its transportation and store. It is necessary to find an effective method to compress the hyperspectral image. Through analysis and comparison of current various algorithms, a mixed compression algorithm based on prediction, integer wavelet transform and embedded zero-tree wavelet (EZW) is proposed in this paper. We adopt a high-powered Digital Signal Processor
Jiming Fan; Jiankang Zhou; Xinhua Chen; Weimin Shen
Summary form only given. The increased information content of hyperspectral imagery over multispectral data has attracted significant interest from the defense and remote sensing communities. We develop a mechanism for compressing hyperspectral imagery with no loss of information. The challenge of hyperspectral image compression lies in the non-isotropy and non-stationarity that is displayed across the spectral channels. Short-range dependence is
S. Srinivasan; L. N. Kanal
ProVision Technologies, a NASA research partnership center at Sternis Space Center in Mississippi, has developed a new hyperspectral imaging (HSI) system that is much smaller than the original large units used aboard remote sensing aircraft and satellites. The new apparatus is about the size of a breadbox. Health-related applications of HSI include non-invasive analysis of human skin to characterize wounds and wound healing rates (especially important for space travelers who heal more slowly), determining if burns are first-, second-, or third degree (rather than painful punch biopsies). The work is sponsored under NASA's Space Product Development (SPD) program.
In the LMM for hyperspectral images, all the image spectra lie on a high-dimensional simplex with corners called endmembers. Given a set of endmembers, the standard calculation of fractional abundances with constrained least squares typically identifies the spectra as combinations of most, if not all, endmembers. We assume instead that pixels are combinations of only a few endmembers, yielding abundance vectors that are sparse. We introduce sparse demixing (SD), which is a method that is similar to orthogonal matching pursuit, for calculating these sparse abundances. We demonstrate that SD outperforms an existing L(1) demixing algorithm, which we prove to depend adversely on the angles between endmembers. We combine SD with dictionary learning methods to calculate automatically endmembers for a provided set of spectra. Applying it to an airborne visible/infrared imaging spectrometer image of Cuprite, NV, yields endmembers that compare favorably with signatures from the USGS spectral library. PMID:21693418
Greer, John B
The design, operation, and performance of the fourth generation of Science and Technology International's Advanced Airborne Hyperspectral Imaging Sensors (AAHIS) are described. These imaging spectrometers have a variable bandwidth ranging from 390-840 nm. A three-axis image stabilization provides spatially and spectrally coherent imagery by damping most of the airborne platform's random motion. A wide 40-degree field of view coupled with sub-pixel detection allows for a large area coverage rate. A software controlled variable aperture, spectral shaping filters, and high quantum efficiency, back-illuminated CCD's contribute to the excellent sensitivity of the sensors. AAHIS sensors have been operated on a variety of fixed and rotary wing platforms, achieving ground-sampling distances ranging from 6.5 cm to 2 m. While these sensors have been primarily designed for use over littoral zones, they are able to operate over both land and water. AAHIS has been used for detecting and locating submarines, mines, tanks, divers, camouflage and disturbed earth. Civilian applications include search and rescue on land and at sea, agricultural analysis, environmental time-series, coral reef assessment, effluent plume detection, coastal mapping, damage assessment, and seasonal whale population monitoring
Topping, Miles Q.; Pfeiffer, Joel E.; Sparks, Andrew W.; Jim, Kevin T. C.; Yoon, Dugan
This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to hyperspectral imaging.
Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.
The continuous control of the conservation state of outdoor materials is a good practice for timely planning conservative interventions and therefore to preserve historical buildings. The monitoring of surfaces composition, in order to characterize compounds of neo-formation and deposition, by traditional diagnostic campaigns, although gives accurate results, is a long and expensive method, and often micro-destructive analyses are required. On the other hand, hyperspectral analysis in the visible and near infrared (VNIR) region is a very common technique for determining the characteristics and properties of soils, air, and water in consideration of its capability to give information in a rapid, simultaneous and not-destructive way. VNIR Hypespectral analysis, which discriminate materials on the basis of their different patterns of absorption at specific wavelengths, are in fact successfully used for identifying minerals and rocks (1), as well as for detecting soil properties including moisture, organic content and salinity (2). Among the existing VNIR techniques (Laboratory Spectroscopy - LS, Portable Spectroscopy - PS and Imaging Spectroscopy - IS), PS and IS can play a crucial role in the characterization of components of exposed stone surfaces. In particular, the Imaging Spectroscopic (remote sensing), which uses sensors placed both on land or airborne, may contribute to the monitoring of large areas in consideration of its ability to produce large areal maps at relatively low costs. In this presentation the application of hyperspectral instruments (mainly PS and IS, not applied before in the field of monumental building diagnostic) to quantify the degradation of carbonate surfaces will be discussed. In particular, considering gypsum as the precursor symptom of damage, many factors which may affect the estimation of gypsum content on the surface will be taken into consideration. Two hyperspectral sensors will be considered: 1) A portable radiometer (ASD-FieldSpec FP Pro spectroradiometer), which continuously acquires punctual reflectance spectra in the range 350-2500 nm, both in natural light conditions and by a contact probe (fixed geometry of shot). This instrument is used on field for the identification of different materials, as well as for the definition of maps (e.g geological maps) if coupled with other hyperspectral instruments. 2) Hyperspectral sensor SIM-GA (Selex Galileo Multisensor Hyperspectral System), a system with spatial acquisition of data which may be used on an earth as well as on an airborne platform. SIM-GA consists of two electro-optical heads, which operate in the VNIR and SWIR regions, respectively, between 400-1000 nm and 1000-2500 nm (3). Although the spectral signature in the VNIR of many minerals is known, the co-presence of more minerals on a surface can affect the quantitative analysis of gypsum. Different minerals, such as gypsum, calcite, weddellite, whewellite, and other components (i.e. carbon particles in black crusts) are, in fact, commonly found on historical surfaces. In order to illustrate the complexity, but also the potentiality of hyperspectral sensors (portable or remote sensing) for the characterization of stone surfaces, a case study, the Facade of Santa Maria Novella in Florence - Italy, will be presented. References 1) R.N. Clark and G.A. Swayze, 1995, "Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice, and snow, and other materials: The USGS Tricorder Algorithm", in "Summaries of the Fifth Annual JPL Airborne Earth Science Workshop", JPL Publication 95-1,1,39-40 2) E. Ben-Dor, K. Patin, A. Banin and A. Karnieli, 2002, "Mapping of several soil properties using DATS-7915 hyperspectral scanner data. A case study over clayely soils in Israel", International Journal of Remote Sensing, 23(6), 1043-1062 3) S. Vettori, M. Benvenuti, M. Camaiti, L. Chiarantini, P. Costagliola, S. Moretti, E. Pecchioni, 2008, "Assessment of the deterioration status of historical buildings by Hyperspectral Imaging techniques&q
Camaiti, Mara; Benvenuti, Marco; Chiarantini, Leandro; Costagliola, Pilar; Moretti, Sandro; Paba, Francesca; Pecchioni, Elena; Vettori, Silvia
The utility of Hyper Spectral Imaging (HSI) passive chemical detection employing wide field, standoff imaging continues to be advanced in detection applications. With a drive for reduced SWaP (Size, Weight, and Power), increased speed of detection and sensitivity, developing a handheld platform that is robust and user-friendly increases the detection capabilities of the end user. In addition, easy to use handheld detectors could improve the effectiveness of locating and identifying threats while reducing risks to the individual. ChemImage Sensor Systems (CISS) has developed the HSI Aperio™ sensor for real time, wide area surveillance and standoff detection of explosives, chemical threats, and narcotics for use in both government and commercial contexts. Employing liquid crystal tunable filter technology, the HSI system has an intuitive user interface that produces automated detections and real-time display of threats with an end user created library of threat signatures that is easily updated allowing for new hazardous materials. Unlike existing detection technologies that often require close proximity for sensing and so endanger operators and costly equipment, the handheld sensor allows the individual operator to detect threats from a safe distance. Uses of the sensor include locating production facilities of illegal drugs or IEDs by identification of materials on surfaces such as walls, floors, doors, deposits on production tools and residue on individuals. In addition, the sensor can be used for longer-range standoff applications such as hasty checkpoint or vehicle inspection of residue materials on surfaces or bulk material identification. The CISS Aperio™ sensor has faster data collection, faster image processing, and increased detection capability compared to previous sensors.
Nelson, Matthew P.; Basta, Andrew; Patil, Raju; Klueva, Oksana; Treado, Patrick J.
The Hyperspectral Imager for the Coastal Ocean (HICO) is the first spaceborne hyperspectral sensor designed specifically for the coastal ocean and estuarial, riverine, or other shallow-water areas. The HICO generates hyperspectral images, primarily over the 400-900 nm spectral range, with a ground sample distance of ?90 m (at nadir) and a high signal-to-noise ratio. The HICO is now operating on the International Space Station (ISS). Its cross-track and along-track fields of view are 42 km (at nadir) and 192 km, respectively, for a total scene area of 8000 km(2). The HICO is an innovative prototype sensor that builds on extensive experience with airborne sensors and makes extensive use of commercial off-the-shelf components to build a space sensor at a small fraction of the usual cost and time. Here we describe the instrument's design and characterization and present early images from the ISS. PMID:21478922
Lucke, Robert L; Corson, Michael; McGlothlin, Norman R; Butcher, Steve D; Wood, Daniel L; Korwan, Daniel R; Li, Rong R; Snyder, Willliam A; Davis, Curt O; Chen, Davidson T
A hyperspectral imaging technique was attempted to classify green tea. Five grades of green tea samples were attempted. A hyperspectral imaging system was developed for data acquisition of tea samples. Principal component analysis was performed on the hyperspectral data to determine three optimal band images. Texture analysis was conducted on each optimal band image to extract characteristic variables. A support vector machine (SVM) was used to construct the classification model. The classification rates were 98% and 95% in the training and prediction sets, respectively. The SVM algorithm shows excellent performance in classification results in contrast with other pattern recognitions classifiers. Overall results show that the hyperspectral imaging technique coupled with a SVM classifier can be efficiently utilized to classify green tea. PMID:19571909
Zhao, Jiewen; Chen, Quansheng; Cai, Jianrong; Ouyang, Qin
Sensor simulation modeling is an important tool for the design of new earth imaging systems. As the input of the model, the characteristics of the synthetic spectral scene image data cube (SSSIDC) play an important role in the accuracy of the simulation. Based on a general sensor simulation model, the effects of SSSIDC resolution, sampling interval (SI), and signal-to-noise ratio (SNR) on simulated data are analyzed. Analysis shows that the simulated data characteristics are a function of the model parameters and the SSSIDC characteristics. The results can be used for evaluating the errors of simulated data, giving criteria for scene image synthesis, and designing appropriate model parameters for expected simulation. Simulation experiments are included to demonstrate the discussed analysis, with the results showing that the analysis is valid. PMID:25322221
Dongxing, Tao; Huijie, Zhao; Guorui, Jia; Yan, Yuan
The proposal requested funds for the computing power to bring hyperspectral image processing into undergraduate and graduate remote sensing courses. This upgrade made it possible to handle more students in these oversubscribed courses and to enhance CSES' summer short course entitled "Hyperspectral Imaging and Data Analysis" provided for government, industry, university and military. Funds were also requested to build field measurement capabilities through the purchase of spectroradiometers, canopy radiation sensors and a differential GPS system. These instruments provided systematic and complete sets of field data for the analysis of hyperspectral data with the appropriate radiometric and wavelength calibration as well as atmospheric data needed for application of radiative transfer models. The proposed field equipment made it possible to team-teach a new field methods course, unique in the country, that took advantage of the expertise of the investigators rostered in three different departments, Geology, Geography and Biology.
Goetz, Alexander F. H.; Steffen, Konrad; Wessman, Carol
The Aerospace Leap-frog Imaging Stationary interferometer for Earth Observation (ALISEO) is a hyperspectral imaging interferometer for Earth remote sensing. The instrument belongs to the class of Sagnac stationary interferometers and acquires the image of the target superimposed to the pattern of autocorrelation functions of the electromagnetic field coming from each pixel. The ALISEO sensor together with the data processing algorithms that retrieve the at-sensor spectral radiance are discussed. A model describing the instrument OPD and interferogram center is also discussed, improving the procedures for phase retrieval and spectral estimation. Images acquired by ALISEO are shown, and examples of retrieved reflectance spectra are presented.
Barducci, Alessandro; Castagnoli, Francesco; Castellini, Guido; Guzzi, Donatella; Lastri, Cinzia; Marcoionni, Paolo; Nardino, Vanni; Pippi, Ivan
This paper proposes a new algorithm for lossless compression of hyperspectral images. In our work we found hyperspectral data have unique characteristic based on spectral context and adjacent pixel spectral vectors (curves) highly correlate with each other. Pearson correlation coefficient is an effective measure of spectral similarity between spectral curves to detect horizontal and vertical spectral edge. Thus, spectral correlation is used to prediction in spectral direction for decorrelation of lossless compression of hyperspectral images. Experiments show the proposed algorithm is effective, and it's more important that it has much lower complexity than other algorithms.
Chen, Liang; Liu, Daizhi; Huang, Shiqi
We propose a new lossy compression algorithm for hyperspectral images, which is based on spectral principal component analysis (PCA), followed by JPEG2000 (JP2K). The approach employs an anomaly-removal model in the compression process to preserve anomalous pixels. Results on two different hyperspectral image scenes show that the new algorithm not only provides good post-compression anomaly-detection performance but also improves rate-
Qian Du; Wei Zhu; James E. Fowler
We propose a new lossy compression algorithm for hyperspectral images, which is based on spectral principal component analysis (PCA), followed by JPEG2000 (JP2K). The approach employs an anomaly-removal model in the compression process to preserve anomalous pixels. Results on two different hyperspectral image scenes show that the new algorithm not only provides good post-compression anomaly-detection performance but also improves rate-distortion
Qian Du; Wei Zhu; James E. Fowler
High computing performance of algorithm analysis is essential in many hyperspectral imaging applications, including automatic target recognition for homeland defense and security, risk\\/hazard prevention and monitoring, wild-land fire tracking and biological threat detection. Despite the growing interest in hyperspectral imaging research, only a few efforts devoted to designing and implementing well-conformed parallel processing solutions currently exist in the open literature.
Antonio Plaza; David Valencia; Javier Plaza; Juan S ´ anchez-Testal; Sergio Mu; Soraya Bl
Remote sensing using satellite and aircraft images are well established technology. Remote sensing application of hyperspectral imaging, however, is relatively new to Malaysian forestry. Through a wide range of wavelengths hyperspectral data are precisely capable to capture narrow bands of spectra. Airborne sensors typically offer greatly enhanced spatial and spectral resolution over their satellite counterparts, and able to control experimental design closely during image acquisition. The first study using hyperspectral imaging for forest inventory in Malaysia were conducted by Professor Hj. Kamaruzaman from the Faculty of Forestry, Universiti Putra Malaysia in 2002 using the AISA sensor manufactured by Specim Ltd, Finland. The main objective has been to develop methods that are directly suited for practical tropical forestry application at the high level of accuracy. Forest inventory and tree classification including development of single spectral signatures have been the most important interest at the current practices. Experiences from the studies showed that retrieval of timber volume and tree discrimination using this system is well and some or rather is better than other remote sensing methods. This article reviews the research and application of airborne hyperspectral remote sensing for forest survey and assessment in Malaysia.
Mohd Hasmadi, I.; Kamaruzaman, J.
This paper discusses the calculation, interpretation, and implications of various radiometric sensitivity metrics for Earth-observing hyperspectral imaging (HSI) sensors. The most commonly used sensor performance metric is signal-to-noise ratio (SNR), from which additional noise equivalent quantities can be computed, including: noise equivalent spectral radiance (NESR), noise equivalent delta reflectance (NE??), noise equivalent delta emittance (NE??), and noise equivalent delta temperature (NE?T). For hyperspectral sensors, these metrics are typically calculated from an at-aperture radiance (typically generated by MODTRAN) that includes both target radiance and non-target (atmosphere and background) radiance. Unfortunately, these calculations treat the entire at-aperture radiance as the desired signal, even when the target radiance is only a fraction of the total (such as when sensing through a long or optically dense atmospheric path). To overcome this limitation, an augmented set of metrics based on contrast signal-to-noise ratio (CNSR) is developed, including their noise equivalent counterparts (CNESR, CNE??, CNE??, and CNE?T). These contrast metrics better quantify sensor performance in an operational environment that includes remote sensing through the atmosphere.
Silny, John F.; Zellinger, Lou
We propose compression algorithms for hyperspectral images with enhanced discriminant features. As the dimension of remotely sensed images increases, the need for efficient compression algorithms for hyperspectral images also increases. However, when hyperspectral images are compressed with conventional image compression algorithms, which have been developed to minimize mean squared errors, discriminant features of the original data may be lost during
Chulhee Lee; Euisun Choi
The Spectral Image Decomposition (SPID) compres- sor uses techniques borrowed from spectral image analysis to achieve a high degree of compression on hyperspectral images. The purpose of this compressor is to provide real-time compres- sion for images captured one line at a time using a \\
Paul Jacobs; Christian Miller; Jared Wolff; Xiuhong Sun; Patrick L. Coronado; Guo-Qiang Zhang
The hyper-spectral image compression is one of the front technologies in the remote sensing. The hyper-spectral image has enormous data quantity, so it is important to research on the hyper-spectral image compression. Based on the spectral LOCO-I prediction algorithm, the hyper-spectral image compression system is completed with the DSP+CPLD technology. The result shows that the system can compress the image
Liu Qianwen; Hu Bingliang
Broadband multispectral imaging can be very helpful in showing differences in energy being radiated and is often employed by NASA satellites to monitor temperature and climate changes. In addition, hyperspectral imaging is ideal for advanced laboratory uses, biomedical imaging, forensics, counter-terrorism, skin health, food safety, and Earth imaging. Lextel Intelligence Systems, LLC, of Jackson, Mississippi purchased Photon Industries Inc., a spinoff company of NASA's Stennis Space Center and the Institute for Technology Development dedicated to developing new hyperspectral imaging technologies. Lextel has added new features to and expanded the applicability of the hyperspectral imaging systems. It has made advances in the size, usability, and cost of the instruments. The company now offers a suite of turnkey hyperspectral imaging systems based on the original NASA groundwork. It currently has four lines of hyperspectral imaging products: the EagleEye VNIR 100E, the EagleEye SWIR 100E, the EagleEye SWIR 200E, and the EagleEye UV 100E. These Lextel instruments are used worldwide for a wide variety of applications including medical, military, forensics, and food safety.
This aiming at improving the lossless compression ratio of hyperspectral image, a three-dimensional LMS (3DLMS) algorithm is first deduced and applied into the field of hyperspectral image compression. A novel adaptive prediction model based on 3DLMS algorithm for lossless compression of hyperspectral image is proposed and optimized by the local casual set mean subtraction method. Experimental results on AVIRIS images
Yonghong Chen; Zelin Shi; Deqiang Li
In 2010 an airborne instrument was assembled to image supraglacial lakes near the Jakobshavn Isbrae of the Greenland Ice Sheet. The instrument was designed to fly on a helicopter, and consists of a hyperspectral imager, a GPS/inertial measurement unit (GPS/IMU), and a data-logging computer. A series of narrow visible optical channels ~13nm wide, such as found in a hyperspectral imager, are theorized to be useful in determining the depths of supraglacial lakes using techniques based on the Beer-Lambert-Bouguer Law. During June, several supraglacial lakes were selected for study each day, based upon MODIS imagery taken during the previous week. Flying over a given lake, several track lines were flown to image both shallow and deep sections of the lake, imaging the full range of depth for future algorithm development. The telescoping instrument mount was constructed to allow the sensor package to be deployed from a helicopter in-flight, with an unobstructed downward-facing field of view. The GPS/IMU records the pointing orientation, altitude, and geographical coordinates of the imager to the data-logger, in order to allow post-flight geo-referencing of the raw hyperspectral imagery. With this geo-referenced spectrum data, a depth map for a given lake can be calculated through reference to a water absorptivity model. This risk-reduction expedition to fly a helicopter-borne hyperspectral imager over the supraglacial lakes of Greenland was a success. The instrument mount for the imager worked as designed, and no vibration issues were encountered. As a result, we have confidence in the instrument platform's performance during future surveys of Greenland's supraglacial lakes. The hyperspectral imager, data acquisition computer, and geo-referencing services are provided by Resonon, Inc. of Bozeman, MT, and the GPS/IMU is manufactured by Cloudcap Technology of Hood River, OR.
Adler, J.; Behar, A. E.; Jacobson, N. T.
Rapidly programmable spatial light modulation devices based on MEMS technology have opened an exciting new arena in spectral imaging: rapidly reprogrammable, high spectral resolution, multi-band spectral filters that enable hyperspectral processing directly in the optical hardware of an imaging sensor. Implemented as a multiplexing spectral selector, a digital micro-mirror device (DMD) can independently choose or reject dozens or hundreds of spectral bands and present them simultaneously to an imaging sensor, forming a complete 2D image. The result is a high-speed, highresolution, programmable spectral filter that gives the user complete control over the spectral content of the image formed at the sensor. This technology enables a wide variety of rapidly reprogrammable operational capabilities within the same sensor including broadband, color, false color, multispectral, hyperspectral and target specific, matched filter imaging. Of particular interest is the ability to implement target-specific hyperspectral matched filters directly into the optical train of the sensor, producing an image highlighting a target within a spectrally cluttered scene in real time without further processing. By performing the hyperspectral image processing at the sensor, such a system can operate with high performance, greatly reduced data volume, and at a fraction of the cost of traditional push broom hyperspectral instruments. Examples of color, false color and target-specific matched-filter images recorded with our visible-spectrum prototype will be displayed, and extensions to other spectral regions will be discussed.
Graff, David L.; Love, Steven P.
Hyperspectral imaging has tremendous potential to detect important molecular biomarkers of early cancer based on their unique spectral signatures. Several drawbacks have limited its use for in vivo screening applications: most notably the poor temporal and spatial resolution, high expense, and low optical throughput of existing hyperspectral imagers. We present the development of a new real-time hyperspectral endoscope (called the image mapping spectroscopy endoscope) based on an image mapping technique capable of addressing these challenges. The parallel high throughput nature of this technique enables the device to operate at frame rates of 5.2 frames per second while collecting a (x, y, ?) datacube of 350 × 350 × 48. We have successfully imaged tissue in vivo, resolving a vasculature pattern of the lower lip while simultaneously detecting oxy-hemoglobin. PMID:21639573
Kester, Robert T.; Bedard, Noah; Gao, Liang; Tkaczyk, Tomasz S.
This paper addresses the main goals and objectives of the Hyperspectral Imaging Network (HYPER-I-NET), a recently started Marie Curie Research Training Network. The project is designed to build an interdisciplinary research community focusing on hyperspectral imaging activities. The core strategy of the network is to create a powerful interdisciplinary synergy between different domains of expertise closely related to hyperspectral imaging
Antonio Plaza; Andreas Mueller; Rudolph Richter; Torbjørn Skauli; Zbynek Malenovsky; Jose Bioucas; S. Hofer; J. Chanussot; C. Jutten; V. Carrere; I. Baarstad; P. Kaspersen; J. Nieke; K. Itten; T. Hyvarinen; P. Gamba; F. Dell'Acqua; J. A. Benediktsson; M. E. Schaepman; J. G. P. W. Clevers; B. Zagajewski
in the development of efficient data processing techniques for hyperspectral image analysis . For instanceChapter 7 Parallel Implementation of Morphological Neural Networks for Hyperspectral Image Analysis- ments have been introduced, in particular, for hyperspectral image data sets with 131 #12;132 High
Plaza, Antonio J.
In this paper, a new algorithm for lossless compres- sion of hyperspectral images is proposed. The spectral redundancy in hyperspectral images is exploited using a context-match method driven by the correlation between adjacent bands. This method is suitable for hyperspectral images in the band-sequential format. Moreover, this method compares favorably with the recent pro- posed lossless compression algorithms in terms
Hongqiang Wang; S. Derin Babacan; Khalid Sayood
-based Hyperspectral Image Compression algorithms for use in space. In this paper we present an implementation and implementation of a space-bound FPGA-based Hyperspectral Image Compression algorithm. We have selected the SetHyperspectral Image Compression on Reconfigurable Platforms1 Thomas W. Fry Department of Electrical
Hyperspectral image segmentation, deblurring, and spectral analysis for material identification-spectral or hyperspectral imagery is generally low resolution, it is possible for pixels in the image to contain several are then tested and compared on hyperspectral images associated with space object material identification
Plemmons, Robert J.
Low-Complexity Principal Component Analysis for Hyperspectral Image Compression Qian Du and James E-detection task. Index Terms-- principal component analysis, hyperspectral image compression, JPEG2000, spectral with JPEG2000 for hyperspectral-image com- pression. However, the computational cost of determining the data
Fowler, James E.
Semi-supervised hyperspectral image segmentation using regionalized stochastic watershed Jes a stochastic watershed-based algorithm for segmenting hyperspectral images using a semi-supervised approach. In the results, besides the generic spatial-spectral segmentation of hyperspectral images, the interest
Paris-Sud XI, UniversitÃ© de
Segmentation and Classification of Hyperspectral Images Using Watershed Transformation Y. Tarabalka. In this paper, we propose to extend the watershed segmentation al- gorithm for hyperspectral images, in order. Experimental segmentation and classification results are presented on two hyperspectral images. It is shown
Paris-Sud XI, UniversitÃ© de
A new compact lightweight imaging system for hyperspectral imaging is described. The system can be thought of as the substitute for traditional mechanical filter-wheel sensor. The system is based on different techniques. It uses an electronic controlled LCTF(liquid crystal tunable filter) which provided rapid and vibrationless selection of any wavelength in the visible to IR range. The imaging system consisted of an optic lens, a CRI VariSpec LCTF and a Dalsa 1M30 camera. First the outline of this system setup is presented, then the optics designed is introduced, next the working principle of LCTF is described in details. A field experiment with the imaging system loaded on an airship was carried out and collected hyperspectral solid image. The images obtained had higher spectral and spatial resolution. Some parts of the 540-600 nm components of the 16-band image cube were also shown. Finally, the data acquired were rough processed to get reflection spectrum(from 420 to 720 nm) of three targets. It is concluded that the experiment has proved that the imaging system is effective in obtaining hyperspectral data. The image captured by the system can be applied to spectral estimation, spectra based classification and spectral based analysis. PMID:19123429
Zhang, Dong-ying; Hong, Jin; Tang, Wei-ping; Yang, Wei-feng; Luo, Jun; Qiao, Yan-li; Zhang, Xie
A hyperspectral imaging system to measure and analyze the reflectance spectra of the human tongue with high spatial resolution is proposed for tongue tumor detection. To achieve fast and accurate performance for detecting tongue tumors, reflectance data were collected using spectral acousto-optic tunable filters and a spectral adapter, and sparse representation was used for the data analysis algorithm. Based on the tumor image database, a recognition rate of 96.5% was achieved. The experimental results show that hyperspectral imaging for tongue tumor diagnosis, together with the spectroscopic classification method provide a new approach for the noninvasive computer-aided diagnosis of tongue tumors. PMID:22368462
Liu, Zhi; Wang, Hongjun; Li, Qingli
PCA (principal components analysis) algorithm is the most basic method of dimension reduction for high-dimensional data1, which plays a significant role in hyperspectral data compression, decorrelation, denoising and feature extraction. With the development of imaging technology, the number of spectral bands in a hyperspectral image is getting larger and larger, and the data cube becomes bigger in these years. As a consequence, operation of dimension reduction is more and more time-consuming nowadays. Fortunately, GPU-based high-performance computing has opened up a novel approach for hyperspectral data processing6. This paper is concerning on the two main processes in hyperspectral image feature extraction: (1) calculation of transformation matrix; (2) transformation in spectrum dimension. These two processes belong to computationally intensive and data-intensive data processing respectively. Through the introduction of GPU parallel computing technology, an algorithm containing PCA transformation based on eigenvalue decomposition 8(EVD) and feature matching identification is implemented, which is aimed to explore the characteristics of the GPU parallel computing and the prospects of GPU application in hyperspectral image processing by analysing thread invoking and speedup of the algorithm. At last, the result of the experiment shows that the algorithm has reached a 12x speedup in total, in which some certain step reaches higher speedups up to 270 times.
Qu, HaiCheng; Zhang, Ye; Lin, Zhouhan; Chen, Hao
High resolution hyperspectral images have important applications in many areas, such as anomaly detection, target recognition and image classification. Due to the limitation of the sensors, it is challenging to obtain high spatial resolution hyperspectral images. Recently, the methods that reconstruct high spatial resolution hyperspectral images from the pair of low resolution hyperspectral images and high resolution RGB image of the same scene have shown promising results. In these methods, sparse non-negative matrix factorization (SNNMF) technique was proposed to exploit the spectral correlations among the RGB and spectral images. However, only the spectral correlations were exploited in these methods, ignoring the abundant spatial structural correlations of the hyperspectral images. In this paper, we propose a novel algorithm combining the structural sparse representation and non-negative matrix factorization technique to exploit the spectral-spatial structure correlations and nonlocal similarity of the hyperspectral images. Compared with SNNMF, our method makes use of both the spectral and spatial redundancies of hyperspectral images, leading to better reconstruction performance. The proposed optimization problem is efficiently solved by using the alternating direction method of multipliers (ADMM) technique. Experiments on a public database show that our approach performs better than other state-of-the-art methods on the visual effect and in the quantitative assessment.
Meng, Guiyu; Li, Guangyu; Dong, Weisheng; Shi, Guangming
A first attempt to exploit distributed source coding (DSC) principles for the lossless compression of hyperspectral images is presented. The DSC paradigm is exploited to design a very light coder which minimizes the exploitation of the correlation between the image bands. In this way we managed to move the computational complexity from the encoder to the decoder, thus matching the
M. Barni; D. Papini; A. Abrardo; E. Magli
MULTISTAGE LATTICE VECTOR QUANTIZATION FOR HYPERSPECTRAL IMAGE COMPRESSION Ying Liu and William A-SPIHT for hyperspectral image compression is presented in Section 4. Section 5 con- cludes the paper. 2. VECTOR]. In this paper, we extended the SPIHT  coding algorithm with lattice vector quantization to code hyperspectral
Hyperspectral imaging (HSI) allows the identification of objects through the analysis of their unique spectral signatures. Although first developed many years ago for use in terrestrial remote sensing, this technology has more recently been studied for application in the medical field. With preliminary data favoring a role for HSI in distinguishing normal and lesional skin tissues, we sought to investigate the potential use of HSI as a diagnostic aid in the classification of atypical Spitzoid neoplasms, a group of lesions that often leave dermatopathologists bewildered. One hundred and two hematoxylin and eosin-stained tissue samples were divided into 1 of 4 diagnostic categories (Spitz nevus, Spitz nevus with unusual features, atypical Spitzoid neoplasm, and Spitzoid malignant melanoma) and 1 of 2 control groups (benign melanocytic nevus and malignant melanoma). A region of interest was selected from the dermal component of each sample, thereby maximizing the examination of melanocytes. Tissue samples were examined at ×400 magnification using a spectroscopy system interfaced with a light microscope. The absorbance patterns of wavelengths from 385 to 880 nm were measured and then analyzed within and among groups. All tissue groups demonstrated 3 common absorbance spectra at 496, 533, and 838 nm. Each sample group contained at least one absorption point that was unique to that group. The Spitzoid malignant melanoma category had the highest number of total and unique absorption points for any sample group. The data were then clustered into 12 representative spectral classes. Although each of the sample groups contained all 12 spectral vectors, they did so in differing proportions. These preliminary results reveal differences in the spectral signatures of the Spitzoid lesions examined in this study. Further investigation into a role for HSI in classifying atypical Spitzoid neoplasms is encouraged. PMID:24247577
Gaudi, Sudeep; Meyer, Rebecca; Ranka, Jayshree; Granahan, James C; Israel, Steven A; Yachik, Theodore R; Jukic, Drazen M
The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.
Greer, John B.; Flake, J. C.
To the casual observer, transient stress results in a variety of physiological changes that can be seen in the face. Although the conditions can be seen visibly, the conditions affect the emissivity and absorption properties of the skin, which imaging spectrometers, commonly referred to as Hyperspectral (HS) cameras, can quantify at every image pixel. The study reported on in this paper, using Hyperspectral cameras, provides a basis for continued study of HS imaging to eventually quantify biometric stress. This study was limited to the visible to near infrared (VNIR) spectral range. Signal processing tools and algorithms have been developed and are described for using HS face data from human subjects. The subjects were placed in psychologically stressful situations and the camera data were analyzed to detect stress through changes in dermal reflectance and emissivity. Results indicate that hyperspectral imaging may potentially serve as a non-invasive tool to measure changes in skin emissivity indicative of a stressful incident. Particular narrow spectral bands in the near-infrared region of the electromagnetic spectrum seem especially important. Further studies need to be performed to determine the optimal spectral bands and to generalize the conclusions. The enormous information available in hyperspectral imaging needs further analysis and more spectral regions need to be exploited. Non-invasive stress detection is a prominent area of research with countless applications for both military and commercial use including border patrol, stand-off interrogation, access control, surveillance, and non-invasive and un-attended patient monitoring.
Nagaraj, Sheela; Quoraishee, Shafik; Chan, Gabriel; Short, Kenneth R.
The Civil Air Patrol (CAP) is procuring Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) systems to increase their search-and-rescue mission capability. These systems are being installed on a fleet of Gippsland GA-8 aircraft, and will position CAP to gain realworld mission experience with the application of hyperspectral sensor and processing technology to search and rescue. The ARCHER system design, data
Brian Stevenson; Rory O'Connor; William Kendall; Alan Stocker; William Schaff; Rick Holasek; Detlev Even; Drew Alexa; John Salvador; Michael Eismann; Robert Mack; Pat Kee; Steve Harris; Barry Karch; John Kershenstein
This thesis addresses two important aspects in hyperspectral image processing: automatic hyperspectral image denoising and unmixing. The first part of this thesis is devoted to a novel automatic optimized vector bilateral filter denoising algorithm, while the remainder concerns nonnegative matrix factorization with deterministic annealing for unsupervised unmixing in remote sensing hyperspectral images. The need for automatic hyperspectral image processing has been promoted by the development of potent hyperspectral systems, with hundreds of narrow contiguous bands, spanning the visible to the long wave infrared range of the electromagnetic spectrum. Due to the large volume of raw data generated by such sensors, automatic processing in the hyperspectral images processing chain is preferred to minimize human workload and achieve optimal result. Two of the mostly researched processing for such automatic effort are: hyperspectral image denoising, which is an important preprocessing step for almost all remote sensing tasks, and unsupervised unmixing, which decomposes the pixel spectra into a collection of endmember spectral signatures and their corresponding abundance fractions. Two new methodologies are introduced in this thesis to tackle the automatic processing problems described above. Vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios. Typical vector bilateral filtering usage does not employ parameters that have been determined to satisfy optimality criteria. This thesis also introduces an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means. The approach is based on posing the filtering problem as one of nonlinear estimation and minimizing the Stein's unbiased risk estimate (SURE) of this nonlinear estimator. Along the way, this thesis provides a plausibility argument with an analytical example as to why vector bilateral filtering outperforms band-wise 2D bilateral filtering in enhancing SNR. Experimental results show that the optimized vector bilateral filter provides improved denoising performance on multispectral images when compared to several other approaches. Non-negative matrix factorization (NMF) technique and its extensions were developed to find part based, linear representations of non-negative multivariate data. They have been shown to provide more interpretable results with realistic non-negative constrain in unsupervised learning applications such as hyperspectral imagery unmixing, image feature extraction, and data mining. This thesis extends the NMF method by incorporating deterministic annealing optimization procedure, which will help solve the non-convexity problem in NMF and provide a better choice of sparseness constrain. The approach is based on replacing the difficult non-convex optimization problem of NMF with an easier one by adding an auxiliary convex entropy constrain term and solving this first. Experiment results with hyperspectral unmixing application show that the proposed technique provides improved unmixing performance compared to other state-of-the-art methods.
This paper presents a Prior Important Band (PIB) algorithm for the compression of hyper-spectral images. The PIB method endows some of the bands with high priority so that the quality of these bands after compression is better than other bands. The rationale behind this approach is that, the bands of a data cube have different amount of information. Some bands
Feipeng Li; Haimai Shao; Guorui Ma; Qianqing Qin; Deren Li
Hyperspectral Imaging: Training Algorithms & Data Generation REU Students: Ping Fung and Carl +exp[-2(( + s))1/2 D / 3]} 1-rlSI +(rl - SI )exp[-2(( + s))1/2 D / 3] Data Generation To apply our Â· Investigate new, more efficient algorithms for data training Â· Apply data generation process to other mineral
Mountziaris, T. J.
Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.
Thompson, David R.; Castano, Rebecca; Bue, Brian; Gilmore, Martha S.
Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.
Xie, Hua; Kiely, Aaron; Klimesh, matthew; Aranki, Nazeeh
Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our profile. Smaller interesting regions can also easily be selected from the hyperspectral images for more detailed study at microscopic scale.
Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel
\\u000a Hyperspectral remote sensing, or known as imaging spectroscopy, is a recently developed technique since the last two decades\\u000a of the 20th century (Chang 2003). Imaging spectroscopy is a relatively fully-fledged experimental tool that has been successfully\\u000a used in the laboratory by physicists and chemists for over 100 years for identification of materials and their composition.\\u000a Absorption features accord to the
Liangpei Zhang; Yanfei Zhong
The huge data volume of hyperspectral image challenges its transportation and store. It is necessary to find an effective method to compress the hyperspectral image. Through analysis and comparison of current various algorithms, a mixed compression algorithm based on prediction, integer wavelet transform and embedded zero-tree wavelet (EZW) is proposed in this paper. We adopt a high-powered Digital Signal Processor (DSP) of TMS320DM642 to realize the proposed algorithm. Through modifying the mixed algorithm and optimizing its algorithmic language, the processing efficiency of the program was significantly improved, compared the non-optimized one. Our experiment show that the mixed algorithm based on DSP runs much faster than the algorithm on personal computer. The proposed method can achieve the nearly real-time compression with excellent image quality and compression performance.
Fan, Jiming; Zhou, Jiankang; Chen, Xinhua; Shen, Weimin
Development of effective food inspection systems is critical in successful implementation of the hazard analysis and critical control points (HACCP) program. Hyperspectral imaging or imaging spectroscopy, which combines techniques of imaging and spectroscopy to acquire spatial and spectral information simultaneously, has great potential in food quality and safety inspection. This paper reviewed the basic principle and features of hyperspectral imaging
Renfu Lu; Yud-Ren Chen
Hyperspectral imaging technology is the foreland of the remote sensing development in the 21st century and is one of the most important focuses of the remote sensing domain. Hyperspectral images can provide much more information than multispectral images do and can solve many problems which can't be solved by multispectral imaging technology. However this advantage is at the cost of
Wenjie Wang; Zhongming Zhao; Haiqing Zhu
Active hyperspectral imaging is a valuable tool in a wide range of applications. One such area is the detection and identification of chemicals, especially toxic chemical warfare agents, through analysis of the resulting absorption spectrum. This work presents a selection of results from a prototype midwave infrared (MWIR) hyperspectral imaging instrument that has successfully been used for compound detection at a range of standoff distances. Active hyperspectral imaging utilises a broadly tunable laser source to illuminate the scene with light at a range of wavelengths. While there are a number of illumination methods, the chosen configuration illuminates the scene by raster scanning the laser beam using a pair of galvanometric mirrors. The resulting backscattered light from the scene is collected by the same mirrors and focussed onto a suitable single-point detector, where the image is constructed pixel by pixel. The imaging instrument that was developed in this work is based around an IR optical parametric oscillator (OPO) source with broad tunability, operating in the 2.6 to 3.7 ?m (MWIR) and 1.5 to 1.8 ?m (shortwave IR, SWIR) spectral regions. The MWIR beam was primarily used as it addressed the fundamental absorption features of the target compounds compared to the overtone and combination bands in the SWIR region, which can be less intense by more than an order of magnitude. We show that a prototype NCI instrument was able to locate hydrocarbon materials at distances up to 15 metres.
Ruxton, K.; Robertson, G.; Miller, W.; Malcolm, G. P. A.; Maker, G. T.; Howle, C. R.
Because of the high data dimensionality of hyperspectral data, it is somehow difficult to directly apply hyperspectral images in classification and target detection. A fusion method of hyperspectral images based on feature images extraction and contourlet analysis is proposed. The algorithm firstly extracts feature images using subspace partition and principal components analysis (PCA), then these feature images are fused using
Chang Weiwei; Guo Lei; Liu Kun; Fu Zhaoyang
This paper reports activities in the development of AOTF Polarimetric Hyperspectral Imaging (PHI) Systems at JPL along with field observation results for illustrating the technology capabilities and advantages in remote sensing. In addition, the technology was also used to measure thickness distribution and structural imperfections of silicon-on-silicon wafers using white light interference phenomenon for demonstrating the potential in scientific and industrial applications.
Cheng, Li-Jen; Mahoney, Colin; Reyes, George; Baw, Clayton La; Li, G. P.
Wideband hyperspectral imaging (WHSI) systems collect simultaneous spectral and spatial imagery across a broad spectrum that includes the visible\\/near infrared (VNIR), short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) regimes. These passive optical systems capture reflected sunlight and thermal emissions from targets enabling the characterization of surface material, thermal properties, propellants, and gaseous emissions when targets are sunlit
Ian S. Robinson; A. Klier
A New Morphological Anomaly Detection Algorithm for Hyperspectral Images and its GPU Implementation, we develop a new morphological algorithm for anomaly detection in hyperspectral images along architectures, and evaluated with regards to other anomaly detection algorithms using hyperspectral data
Plaza, Antonio J.
Sensing Signal and Image Processing Laboratory 1000 Hilltop Circle, Baltimore, MD 21250 USA cchangLow-bit rate exploitation-based lossy hyperspectral image compression Chein-I Chang Bharath. Hyperspectral image compression has become increasingly important in data exploitation because of enormous data
Plaza, Antonio J.
Overview of Hierarchical Models for Hyperspectral Image Classification Yuliya Tarabalka INRIA, AYIN team 06902 Sophia Antipolis, France email@example.com Abstract Hyperspectral imaging enables the correlations between spa- tially adjacent pixels for accurate image analysis. This paper provides an overview
systems. A whisk broom imaging spectrometer uses a diffraction grating in front of a line of detectorsSnapshot dual-band visible hyperspectral imaging spectrometer John Hartke, MEMBER SPIE United hyperspectral imaging spectrometer. A commercially available digital camera was integrated into a computed
Dereniak, Eustace L.
In this paper, we propose compression algorithms for hyperspectral images with pre-emphasizing discriminantly dominant features. When hyperspectral images are compressed using a conventional image compression algorithm, which has been developed to minimize mean squared errors, discriminant features of the original data may be lost during compression process since such discriminant features may not be large in energies. In order to
Chulhee Lee; Euisun Choi
Hyperspectral image compression has received considerable interest in recent years due to the enormous data volumes collected by imaging spectrometers for Earth Observation. JPEG2000 is an important technique for data compression which has been successfully used in the context of hyperspectral image compression, either in lossless and lossy fashion. Due to the increasing spatial, spectral and temporal resolution of remotely
Milosz Ciznicki; Krzysztof Kurowski; Antonio Plaza
In this paper, we propose two compression methods for hyperspectral images with discriminant features enhanced. Generally, when hyperspectral images are compressed with conventional image compression algorithms, which mainly minimize mean squared errors, discriminant features of the original data may not be well preserved since they may not be necessarily large in energy. In this paper, we propose two compression methods
Chulhee Lee; Euisun Choi; Taeuk Jeong; Sangwook Lee; Jonghwa Lee
Hyperspectral imaging describes the technique of photographing an object using several well-defined optical bands in a broad spectral range. Using satellite- and aircraft-based instruments, macro-scale hyperspectral imaging is well established for geo-observation with applications in many different fields such as geology, archaeology and defence. Microscope-based hyperspectral instruments and analysis techniques are successfully employed in bio-medical research. In order to transfer
R. Padoan; A. G. Steemers; M. E. Klein; B. J. Aalderink; G. de Bruin
A Forward Looking Interferometer (FLI) sensor has the potential to be used as a means of detecting aviation hazards in flight. One of these hazards is mountain wave turbulence. The results from a data acquisition activity at the University of Colorado s Mountain Research Station will be presented here. Hyperspectral datacubes from a Telops Hyper-Cam are being studied to determine if evidence of a turbulent event can be identified in the data. These data are then being compared with D&P TurboFT data, which are collected at a much higher time resolution and broader spectrum.
Lane, Sarah E.; West, Leanne L.; Gimmestad, Gary G.; Kireev, Stanislav; Smith, William L., Sr.; Burdette, Edward M.; Daniels, Taumi; Cornman, Larry
The characterization and calibration of hyperspectral imagers is a challenging one that is expected to become even more challenging as needs increase for highly-accurate radiometric data from such systems. The preflight calibration of the Advanced Responsive Tactically Effective Military Imaging Spectrometer (ARTEMIS) is used as an example of the difficulties to calibrate hyperspectrally. Results from a preflight solar radiation-based calibration are presented with a discussion of the uncertainties in such a method including the NISI-traceable and SItraceable aspects. Expansion on the concept of solar-based calibration is given with descriptions of methods that view the solar disk directly, illuminate a solar diffuser that is part of the sensor's inflight calibration, and illuminate an external diffuser that is imaged by the sensor. The results of error analysis show that it is feasible to achieve preflight calibration using the sun as a source at the same level of uncertainty as those of lamp-based approaches. The error analysis is evaluated and verified through the solar-radiation-based calibration of several of laboratory grade radiometers. Application of these approaches to NASA's upcoming CLARREO mission are discussed including proposed methods for significantly reducing the uncertainties to allow CLARREO data to be used for climate data records.
Hyperspectral images (spectral signatures acquired in hundreds of narrow, contiguous band passes on a regular spatial grid over a target area) have long been utilized in planetary astronomy for remote geochemical analyses. Typical hyperspectral imagery spans the visible to near-and-thermal-infrared wavelengths with 5-20 nm (?/?? > 100) resolution, sufficient to resolve the discriminating spectral features of (near-)surface compounds. Compared with broad-band, multi-spectral imagery, hyperspectral data brings a phase change in the complexity of spectral patterns and the cluster structure and richness of the data space, and consequently in the analysis challenges for tasks like clustering, classification, regression, and parameter inference. Many traditional favorite techniques do not meet these challenges if one’s aim is to fully exploit the rich, intricate information captured by the sensor, ensure discovery of surprising small anomalies, and more. In stellar astronomy, where Ångström resolution is typical, the data complexity can grow even higher. With the advent of 21st century observatories such as ALMA, high spatial and spectral resolution image cubes with thousands of bands are extending into new and wider wavelength domains, adding impetus to develop and deploy increasingly powerful and efficient knowledge extraction techniques. In this talk I will highlight applications of brain-like machine learning, specifically advanced forms of neural maps that mimic analogous behaviors in natural neural maps in brains (for example, preferential attention to rare signals, to enhance discovery of small clusters). I will present examples of information extraction from hyperspectral data in planetary astronomy, and point out advantages over more traditional techniques, for “precision” data mining, discovery of small anomalies in the face of highly irregular cluster structure, accurate inference of non-linearly entangled latent parameters, or non-linear dimension reduction. These works were done in close collaboration with colleagues in planetary science and astronomy, supported in part by the Applied Information Systems Research Program.
The effectiveness of a hyperspectral imaging system integrated on an enhanced dark-field microscope for probing the microscale morphology of model poly(3- hexylthiopene): [6,6]-phenyl-C61- butyric acid methyl ester (P3HT:PCBM) blends is demonstrated. This non-contact technique provides both spectral and spatial information in one measurement, providing an effective mapping of the presence and location of the component materials in the investigated P3HT:PCBM blends spincoated over different substrates (zinc oxide, poly(3,4- ethylenedioxythiophene):poly(styrenesulfonate). The hyperspectral analysis accounts for the micro-scale morphology of P3HT:PCBM blends, even in case of high film roughness, and the quantitative determination of blend components reveals a preferential accumulation of the lowenergy material (P3HT) at the interface with air, confirming the findings reported with other mapping techniques
Torreggiani, Armida; Tinti, Francesca; Savoini, Alberto; Melchiorre, Michele; Po, Riccardo; Camaioni, Nadia
Spectral imaging technologies have been developed rapidly during the past decade. This paper presents hyperspectral and multispectral imaging technologies in the area of food safety and quality evaluation, with an introduction, demonstration, and summarization of the spectral imaging techniques avai...
Image segmentation and clustering is a method to extract a set of components whose members are similar in some way. Instead of focusing on the consistencies of local image characteristics such as borders and regions in a perceptual way, the spectral graph theoretic approach is based on the eigenvectors of an affinity matrix; therefore it captures perceptually important non-local properties of an image. A typical spectral graph segmentation algorithm, normalized cuts, incorporates both the dissimilarity between groups and similarity within groups by capturing global consistency making the segmentation process more balanced and stable. For spectral graph partitioning, we create a graph-image representation wherein each pixel is taken as a graph node, and two pixels are connected by an edge based on certain similarity criteria. In most cases, nearby pixels are likely to be in the same region, therefore each pixel is connected to its spatial neighbors in the normalized cut algorithm. However, this ignores the difference between distinct groups or the similarity within a group. A hyperspectral image contains high spatial correlation among pixels, but each pixel is better described by its high dimensional spectral feature vector which provides more information when characterizing the similarities among every pair of pixels. Also, to facilitate the fact that boundary usually resides in low density regions in spectral domain, a local density adaptive affinity matrix is presented in this paper. Results will be shown for airborne hyperspectral imagery collected with the HyMAP, AVIRIS, HYDICE sensors.
Fan, Lei; Messinger, David W.
The method to evaluate the grade of the pork based on hyperspectral imaging techniques was studied. Principal component analysis (PCA) was performed on the hyperspectral image data to extract the principal components which were used as the inputs of the evaluation model. By comparing the different discriminating rates in the calibration set and the validation set under different information, the choice of the components can be optimized. Experimental results showed that the classification evaluation model was the optimal when the principal of component (PC) of spectra was 3, while the corresponding discriminating rate was 89.1% in the calibration set and 84.9% in the validation set. It was also good when the PC of images was 9, while the corresponding discriminating rate was 97.2% in the calibration set and 91.1% in the validation set. The evaluation model based on both information of spectra and images was built, in which the corresponding PCs of spectra and images were used as the inputs. This model performed very well in grade classification evaluation, and the discriminating rates of calibration set and validation set were 99.5% and 92.7%, respectively, which were better than the two evaluation models based on single information of spectra or images.
Zhou, Rui; Cai, Bo; Wang, Shoubing; Ji, Huihua; Chen, Huacai
The nonlinear dimensionality reduction and its effects on vector classification and segmentation of hyperspectral images are investigated in this letter. In particular, the way dimensionality reduction influences and helps classification and segmentation is studied. The proposed framework takes into account the nonlinear nature of high-dimensional hyperspectral images and projects onto a lower dimensional space via a novel spatially coherent locally
Anish Mohan; Guillermo Sapiro; Edward Bosch
1 Successive Approximation Wavelet Coding of AVIRIS Hyperspectral Images Alessandro J. S. Dutra This work presents compression algorithms which build on a state-of-the-art codec, the Set Partitioned hyperspectral images, which possess 224 spectral bands. The first proposed method, LVQ-SPECK, uses a lattice
A clustered differential pulse code modulation lossless compression method for hyperspectral images is presented. The spectra of a hyperspectral image is clustered, and an optimized predictor is calculated for each cluster. Prediction is performed using a linear predictor. After prediction, the difference between the predicted and original values is computed. The difference is entropy-coded using an adaptive entropy coder for
Jarno Mielikainen; Pekka Toivanen
Hyperspectral image has weak spatial correlation and strong spectral correlation. As to exploit spectrum redundancy sufficiently, it must be pre-processed. In this paper, a new algorithm for lossless compression of hyperspectral images based on adaptive band regrouping is proposed. Firstly, the affinity propagation clustering algorithm (AP) is chosen for band regrouping according to interband correlation. Then a linear prediction algorithm
Mingyi He; Lin Bai; Yuchao Dai; Jing Zhang
Estimates of vegetation water content are of great interest for assessing vegetation water status in agriculture and forestry, and have been used for drought assessment. This study focuses on the retrieval of foliar water content with hyperspectral data at canopy level. The hyperspectral image used in this study was acquired by the airborne operative modular imaging spectrometer (OMIS) at Demonstration
Xia Zhang; Quanjun Jiao; Di Wu; Bing Zhang; Lianru Gao
A hyperspectral imaging system was developed to detect problem hatching eggs (non-fertile or dead embryos) prior to or during early incubation and to detect table eggs with blood spots and cracked shells. All eggs were imaged using a hyperspectral camera system (wavelengths detected from 400-900mm) ...
During last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there have been renewed interests in using hyperspectral imaging in quality evaluation of different food products. The
Gamal Elmasry; Douglas F. Barbin; Da-Wen Sun; Paul Allen
A hyperspectral imaging system in simultaneous reflectance (400-675 nm) and transmittance (675-1000 nm) modes was developed for detection of hollow or bloater damage on whole pickles. Hyperspectral reflectance and transmittance images were acquired from normal and bloated whole pickle samples collec...
During the last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there has been a renewed interest in using hyperspectral imaging in quality evaluation of different food
Gamal Elmasry; Douglas F. Barbin; Da-Wen Sun; Paul Allen
A fractal-based image compression algorithm under wavelet transformation for hyper-spectral remote sensing image was introduced in this paper (also named AWFC algorithm). With the development of the hyperspectral remote sensing we have to obtain more and more spectral bands and how to store and transmit the huge data measured by TB bits level becomes a disaster to the limited electrical bandwidth. It is important to compress the huge hyperspectral image data acquired by hyperspectral sensor such as MODIS, PHI, OMIS etc. Otherwise, conventional lossless compression algorithm couldn't reach satisfied compression ratio while other loss compression methods could get results of high compression ratio but no good image fidelity especially to the hyperspectral image data. As the third generation image compression algorithm-fractal image compression is superior than traditional compression methods with high compression ratio, good image fidelity and less time complexity. In order to keep the spectral dimension invariability, we have compared the results of two compression algorithms based on the outside storage file structure of BSQ and BIP separately. The HV and Quad-tree partitioning and the domain-range matching algorithms have also been improved to accelerate the encode/decode efficiency. The proposed method has been realized and obtained perfect experimental results. At last, the possible modifications algorithm and the limitations of the method are also analyzed and discussed in this paper.
Hu, Xingtang; Zhang, Bing; Zhang, Xia; Hu, Fangchao; Wei, Zheng
As we enter a new era of using satellite hyperspectral sensors for weather and other environmental applications, this paper discusses the applicability of using IR hyperspectral data for climate change monitoring; in particular, for quantifying the greenhouse effects. While broadband 1st order statistics quantify radiative forcings, the IR hyperspectral data provides a means of monitoring feedback processes. Radiative transfer modeling
Hsiao-hua Burke; Bill Snow; Kris Farrar
Fluorescence hyperspectral imaging is increasingly being used for food quality inspection and detection of potential food safety concerns. The flexible nature of a self-scanning pushbroom hyperspectral imager lends itself to these kinds of applications, among others. To increase the use of this technique there has been a tendency to use low cost off-the-shelf hyperspectral sensors which are typically not radiometrically calibrated. To ensure that these systems are optimized for response and repeatability, it is imperative that the systems be both radiometrically and spectrally calibrated specifically for fluorescence imaging. Fluorescence imaging provides several challenges such as low signal, stray light and a low signal dynamic range that are improved with careful radiometric calibration. A radiometric and spectral approach that includes flat fielding and the conversion of digital number responses to radiance for calibrating this imaging system and other types of hyperspectral imagers is described in this paper. Results show that this method can be adopted for calibrating fluorescence and reflective hyperspectral imaging systems in the visible and near infra-red domains.
Ononye, Ambrose E.; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell
High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of the FPGAs makes it possible to effectively alter the design to some extent to satisfy different requirements without adding hardware. The implementation could be easily propagated to future FPGA generations and/or to custom application-specific integrated circuits.
Aranki, Nazeeh; Namkung, Jeffrey; Villapando, Carlos; Kiely, Aaron; Klimesh, Matthew; Xie, Hua
Software was developed that automatically detects minerals that are present in each pixel of a hyperspectral image. An algorithm based on sparse spectral unmixing with Bayesian Positive Source Separation is used to produce mineral abundance maps from hyperspectral images. A superpixel segmentation strategy enables efficient unmixing in an interactive session. The algorithm computes statistically likely combinations of constituents based on a set of possible constituent minerals whose abundances are uncertain. A library of source spectra from laboratory experiments or previous remote observations is used. A superpixel segmentation strategy improves analysis time by orders of magnitude, permitting incorporation into an interactive user session (see figure). Mineralogical search strategies can be categorized as supervised or unsupervised. Supervised methods use a detection function, developed on previous data by hand or statistical techniques, to identify one or more specific target signals. Purely unsupervised results are not always physically meaningful, and may ignore subtle or localized mineralogy since they aim to minimize reconstruction error over the entire image. This algorithm offers advantages of both methods, providing meaningful physical interpretations and sensitivity to subtle or unexpected minerals.
Castano, Rebecca; Thompson, David R.; Gilmore, Martha
The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several applications exist, however, where having the desired information calculated quickly enough for practical use is highly desirable. High computing performance of algorithm analysis is particularly important in homeland defense and security applications, in which swift decisions often involve detection of (sub-pixel) military targets (including hostile weaponry, camouflage, concealment, and decoys) or chemical/biological agents. In order to speed-up computational performance of hyperspectral imaging algorithms, this paper develops several fast parallel data processing techniques. Techniques include four classes of algorithms: (1) unsupervised classification, (2) spectral unmixing, and (3) automatic target recognition, and (4) onboard data compression. A massively parallel Beowulf cluster (Thunderhead) at NASA's Goddard Space Flight Center in Maryland is used to measure parallel performance of the proposed algorithms. In order to explore the viability of developing onboard, real-time hyperspectral data compression algorithms, a Xilinx Virtex-II field programmable gate array (FPGA) is also used in experiments. Our quantitative and comparative assessment of parallel techniques and strategies may help image analysts in selection of parallel hyperspectral algorithms for specific applications.
Plaza, Antonio; Chang, Chein-I.; Plaza, Javier; Valencia, David
A Hyperspectral image is a sequence of images generated by collecting contiguously spaced spectral bands of data. One can view such an image sequence as a three-dimensional array of intensity values (pixels) within a rectangular prism. We present a Three-Dimensional Set Partitioned Embedded bloCK (3DSPECK) algorithm based on the observation that hyperspectral images are contiguous in the spectrum axis (this
Xiaoli Tang; William A. Pearlman; James W. Modestino
Active volcanoes occur on every continent, often in close proximity to heavily populated areas. While ground-based studies are essential for scientific research and disaster mitigation, remote sensing from space can provide rapid and continuous monitoring of active and potentially active volcanoes [Ramsey and Flynn, 2004]. In this paper, we report on hyperspectral measurements of Kilauea volcano, Hawaii. Hyperspectral images obtained by the US Air Force TacSat-3/ARTEMIS sensor [Lockwood et al, 2006] are used to obtain estimates of the surface temperatures for the volcano. ARTEMIS measures surface-reflected light in the visible, near-infrared, and short-wave infrared bands (VNIR-SWIR). The SWIR bands are known to be sensitive to thermal radiation [Green, 1996]. For example, images from the NASA Hyperion hyperspectral sensor have shown the extent of wildfires and active volcanoes [Young, 2009]. We employ the methodology described by Dennison et al, (2006) to obtain an estimate of the temperature of the active region of Kilauea. Both day and night-time images were used in the analysis. To improve the estimate, we aggregated neighboring pixels. The active rim of the lava lake is clearly discernable in the temperature image, with a measured temperature exceeding 1100o C. The temperature decreases markedly on the exterior of the summit crater. While a long-wave infrared (LWIR) sensor would be ideal for volcano monitoring, we have shown that the thermal state of an active volcano can be monitored using the SWIR channels of a reflective hyperspectral imager. References: Dennison, Philip E., Kraivut Charoensiri, Dar A. Roberts, Seth H. Peterson, and Robert O. Green (2006). Wildfire temperature and land cover modeling using hyperspectral data, Remote Sens. Environ., vol. 100, pp. 212-222. Green, R. O. (1996). Estimation of biomass fire temperature and areal extent from calibrated AVIRIS spectra, in Summaries of the 6th Annual JPL Airborne Earth Science Workshop, Pasadena, CA JPL Publ. 96-4, vol. 1, pp. 105-113. Lockwood, Ronald B., Thomas W. Cooley, Richard M. Nadile, James A. Gardner, Peter S. Armstrong, Abraham M. Payton, Thom M. Davis, Stanley D. Straight, Thomas G. Chrien, Edward L. Gussin, and David Makowski (2006). Advanced Responsive Tactically-Effective Military Imaging Spectrometer (ARTEMIS) Design, in Proceedings of the 2006 IEEE International Geoscience and Remote Sensing Symposium, 31 July-4 August 2006, Denver, Colorado. Ramsey, Michael S., and Luke P. Flynn (2004). Strategies, insights, and the recent advances in volcanic monitoring and mapping with data from NASA’s Earth Observing System, Jour. of Volcanology and Geothermal Research, vol. 135, pp. 1-11. Young, Joseph (2009). EO-1 Weekly status report for September 24-30, 2009, Earth Science Mission Operations (ESMO) Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771.
Cipar, J. J.; Dunn, R.; Cooley, T.
Retrieval of reflectance spectra as well as of other level 2 products from hyperspectral remotely sensed data demands an accurate analysis of the attenuation and scattering effects due to aerosols and gases distributed in the atmospheric path. Starting from radiometrically corrected data, target reflectance spectra were obtained by solving the radiative transfer equation using a rather simple physical model, which also takes into account the effects of molecular (Rayleigh) and aerosol (Mie) scattering. We have also investigated the problem of how the environment surrounding the observed target may affect the radiance reaching the imaging sensor. By comparing simulated with measured data improvements in visible and near infrared spectral range with respect to the usually adopted inversion techniques are shown. Some examples of atmospheric correction on data acquired by the MIVIS imaging spectrometer, are presented and discussed.
Barducci, Alessandro; Guzzi, Donatella; Marcoionni, Paolo; Pippi, Ivan
1 Monitoring Phosphorus Content in a Tropical Estuary Lagoon using an Hyperspectral Sensor and its Remote Sensing, Total Phosphorus Contamination, Non-Point Source Pollution Problem and Research pollution with satellite imaging could provide important information related to the total phosphorus (TP
The system is a single hyperspectral imaging instrument that has the unique capability to acquire both fluorescence and reflectance high-spatial-resolution data that is inherently spatially and spectrally registered. Potential uses of this instrument include plant stress monitoring, counterfeit document detection, biomedical imaging, forensic imaging, and general materials identification. Until now, reflectance and fluorescence spectral imaging have been performed by separate instruments. Neither a reflectance spectral image nor a fluorescence spectral image alone yields as much information about a target surface as does a combination of the two modalities. Before this system was developed, to benefit from this combination, analysts needed to perform time-consuming post-processing efforts to co-register the reflective and fluorescence information. With this instrument, the inherent spatial and spectral registration of the reflectance and fluorescence images minimizes the need for this post-processing step. The main challenge for this technology is to detect the fluorescence signal in the presence of a much stronger reflectance signal. To meet this challenge, the instrument modulates artificial light sources from ultraviolet through the visible to the near-infrared part of the spectrum; in this way, both the reflective and fluorescence signals can be measured through differencing processes to optimize fluorescence and reflectance spectra as needed. The main functional components of the instrument are a hyperspectral imager, an illumination system, and an image-plane scanner. The hyperspectral imager is a one-dimensional (line) imaging spectrometer that includes a spectrally dispersive element and a two-dimensional focal plane detector array. The spectral range of the current imaging spectrometer is between 400 to 1,000 nm, and the wavelength resolution is approximately 3 nm. The illumination system consists of narrowband blue, ultraviolet, and other discrete wavelength light-emitting-diode (LED) sources and white-light LED sources designed to produce consistently spatially stable light. White LEDs provide illumination for the measurement of reflectance spectra, while narrowband blue and UV LEDs are used to excite fluorescence. Each spectral type of LED can be turned on or off depending on the specific remote-sensing process being performed. Uniformity of illumination is achieved by using an array of LEDs and/or an integrating sphere or other diffusing surface. The image plane scanner uses a fore optic with a field of view large enough to provide an entire scan line on the image plane. It builds up a two-dimensional image in pushbroom fashion as the target is scanned across the image plane either by moving the object or moving the fore optic. For fluorescence detection, spectral filtering of a narrowband light illumination source is sometimes necessary to minimize the interference of the source spectrum wings with the fluorescence signal. Spectral filtering is achieved with optical interference filters and absorption glasses. This dual spectral imaging capability will enable the optimization of reflective, fluorescence, and fused datasets as well as a cost-effective design for multispectral imaging solutions. This system has been used in plant stress detection studies and in currency analysis.
Ryan, Robert E.; O'Neal, S. Duane; Lanoue, Mark; Russell, Jeffrey
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for progressive band selection for hyperspectral images. A system having module configured to control a processor to practice the method calculates a virtual dimensionality of a hyperspectral image having multiple bands to determine a quantity Q of how many bands are needed for a threshold level of information, ranks each band based on a statistical measure, selects Q bands from the multiple bands to generate a subset of bands based on the virtual dimensionality, and generates a reduced image based on the subset of bands. This approach can create reduced datasets of full hyperspectral images tailored for individual applications. The system uses a metric specific to a target application to rank the image bands, and then selects the most useful bands. The number of bands selected can be specified manually or calculated from the hyperspectral image's virtual dimensionality.
Fisher, Kevin (Inventor)
Detection and identification of Toxic industrial chemicals (TICs) represent a major challenge to protect and sustain first responder and public security. In this context, passive Hyperspectral Imaging (HSI) is a promising technology for the standoff detection and identification of chemical vapors emanating from a distant location. To investigate this method, the Department of National Defense and Public Safety Canada have mandated Defense Research and Development Canada (DRDC) - Valcartier to develop and test Very Long Wave Infrared (VLWIR) HSI sensors for standoff detection. The initial effort was focused to address the standoff detection and identification of toxic industrial chemicals (TICs), surrogates and precursors. Sensors such as the Improved Compact ATmospheric Sounding Interferometer (iCATSI) and the Multi-option Differential Detection and Imaging Fourier Spectrometer (MoDDIFS) were developed for this application. This paper presents the sensor developments and preliminary results of standoff detection and identification of TICs and precursors. The iCATSI and MoDDIFS sensors are based on the optical differential Fourier-transform infrared (FTIR) radiometric technology and are able to detect, spectrally resolve and identify small leak at ranges in excess of 1 km. Results from a series of trials in asymmetric threat type scenarios are reported. These results serve to establish the potential of passive standoff HSI detection of TICs, precursors and surrogates.
Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Dubé, Denis
Toxic industrial chemicals (TICs) represent a major threat to public health and security. Their detection constitutes a real challenge to security and first responder's communities. One promising detection method is based on the passive standoff identification of chemical vapors emanating from the laboratory under surveillance. To investigate this method, the Department of National Defense and Public Safety Canada have mandated Defense Research and Development Canada (DRDC) - Valcartier to develop and test passive Long Wave Infrared (LWIR) hyperspectral imaging (HSI) sensors for standoff detection. The initial effort was focused to address the standoff detection and identification of toxic industrial chemicals (TICs) and precursors. Sensors such as the Multi-option Differential Detection and Imaging Fourier Spectrometer (MoDDIFS) and the Improved Compact ATmospheric Sounding Interferometer (iCATSI) were developed for this application. This paper describes the sensor developments and presents initial results of standoff detection and identification of TICs and precursors. The standoff sensors are based on the differential Fourier-transform infrared (FTIR) radiometric technology and are able to detect, spectrally resolve and identify small leak plumes at ranges in excess of 1 km. Results from a series of trials in asymmetric threat type scenarios will be presented. These results will serve to establish the potential of the method for standoff detection of TICs precursors and surrogates.
Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Dubé, Denis
Hyperspectral image has weak spatial correlation and strong spectral correlation. As to exploit spectrum redundancy sufficiently, it must be pre-processed. In this paper, a new algorithm for lossless compression of hyperspectral images based on adaptive band regrouping is proposed. Firstly, the affinity propagation clustering algorithm (AP) is chosen for band regrouping according to interband correlation. Then a linear prediction algorithm based on context prediction is applied to the hyperspectral images in different groups. Finally, the experimental results show that the proposed algorithm achieves performance gains of 1.12bpp over the conventional algorithm.
He, Mingyi; Bai, Lin; Dai, Yuchao; Zhang, Jing
In general, hyper-spectral sensor, LiDAR and high spatial resolution satellite imagery for underwater monitoring are dependent on water clarity or water transparency that can be measured using a Secchi disk or satellite ocean color data. Optical properties in the sea waters of South Korea are influenced mainly by a strong tide and oceanic currents, diurnal, daily and seasonal variations of water transparency. The satellite-based Secchi depth (ZSD) analysis showed the applicability of hyper-spectral sensor, LiDAR and optical satellite, determined by the location connected with the local distribution of Case 1 and 2 waters. The southeast coastal areas of Jeju Island are selected as test sites for a combined underwater experiment, because those areas represent Case 1 water. Study area is a small port (<15m) in the southeast area of the island and linear underwater target used by sewage pipe is located in this area. Our experiments are as follows: 1. atmospheric and sun-glint correction methods to improve the underwater monitoring ability; 2. intercomparison of water depths obtained from three different sensors. Three sensors used here are the CASI-1500 (Wide-Array Airborne Hyperspectral VNIR Imager (0.38-1.05 microns), the Coastal Zone Mapping and Imaging Lidar (CZMIL) and Korean Multi-purpose Satellite-3 (KOMPSAT-3) with 2.8 meter multi-spectral resolution. The experimental results were affected by water clarity and surface condition, and the bathymetric results of three sensors show some differences caused by sensor-itself, bathymetric algorithm and tide level. It is shown that CASI-1500 was applicable for bathymetry and underwater target detection in this area, but KOMPSAT-3 should be improved for Case 1 water. Although this experiment was designed to compare underwater monitoring ability of LIDAR, CASI-1500, KOMPSAT-3 data, this paper was based on initial results and suggested only results about the bathymetry and underwater target detection.
Yang, Chan-Su; Kim, Sun-Hwa
We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths. PMID:21483616
Larusson, Fridrik; Fantini, Sergio; Miller, Eric L.
A new generation of narrow-band hyperspectral remote sensing data offers an alternative to broad-band multispectral data for the estimation of vegetation chlorophyll content. This paper examines the potential of some of these sensors comparing red-edge and simple ratio indices to develop a rapid and cost-effective system for monitoring Mediterranean pine plantations in Spain. Chlorophyll content retrieval was analyzed with the red-edge R750/R710 index and the simple ratio R800/R560 index using the PROSPECT-5 leaf model and the Discrete Anisotropic Radiative Transfer (DART) and experimental approach. Five sensors were used: AHS, CHRIS/Proba, Hyperion, Landsat and QuickBird. The model simulation results obtained with synthetic spectra demonstrated the feasibility of estimating Ca + b content in conifers using the simple ratio R800/R560 index formulated with different full widths at half maximum (FWHM) at the leaf level. This index yielded a r2 = 0.69 for a FWHM of 30 nm and r2 = 0.55 for a FWHM of 70 nm. Experimental results compared the regression coefficients obtained with various multispectral and hyperspectral images with different spatial resolutions at the stand level. The strongest relationships where obtained using high-resolution hyperspectral images acquired with the AHS sensor (r2 = 0.65) while coarser spatial and spectral resolution images yielded a lower root mean square error (QuickBird r2 = 0.42; Landsat r2 = 0.48; Hyperion r2 = 0.56; CHRIS/Proba r2 = 0.57). This study shows the need to estimate chlorophyll content in forest plantations at the stand level with high spatial and spectral resolution sensors. Nevertheless, these results also show the accuracy obtained with medium-resolution sensors when monitoring physiological processes. Generating biochemical maps at the stand level could play a critical rule in the early detection of forest decline processes enabling their use in precision forestry.
Navarro-Cerrillo, Rafael Mª; Trujillo, Jesus; de la Orden, Manuel Sánchez; Hernández-Clemente, Rocío
Surgical burn treatment depends on accurate estimation of burn depth. Many methods have been used to asses burns, but none has gained wide acceptance. Hyperspectral imaging technique has recently entered the medical research field with encouraging results. In this paper we present a preliminary study (case presentation) that aims to point out the value of this optical method in burn wound characterization and to set up future lines of investigation. A hyperspectral image of a leg and foot with partial thickness burns was obtained in the fifth postburn day. The image was analyzed using linear spectral unmixing model as a tool for mapping the investigated areas. The article gives details on the mathematical bases of the interpretation model and correlations with clinical examination pointing out the advantages of hyperspectral imaging technique. While the results were encouraging, further more extended and better founded studies are being prepared before recognizing hyperspectral imaging technique as an applicable method of burn wound assessment. PMID:24997530
Calin, Mihaela Antonina; Parasca, Sorin Viorel; Savastru, Roxana; Manea, Dragos
Diabetic foot ulceration is a major complication of diabetes and afflicts as many as 15 to 25% of type 1 and 2 diabetes patients during their lifetime. If untreated, diabetic foot ulcers may become infected and require total or partial amputation of the affected limb. Early identification of tissue at risk of ulcerating could enable proper preventive care, thereby reducing the incidence of foot ulceration. Furthermore, noninvasive assessment of tissue viability around already formed ulcers could inform the diabetes caregiver about the severity of the wound and help assess the need for amputation. This article reviews how hyperspectral imaging between 450 and 700 nm can be used to assess the risk of diabetic foot ulcer development and to predict the likelihood of healing noninvasively. Two methods are described to analyze the in vivo hyperspectral measurements. The first method is based on the modified Beer-Lambert law and produces a map of oxyhemoglobin and deoxyhemoglobin concentrations in the dermis of the foot. The second is based on a two-layer optical model of skin and can retrieve not only oxyhemoglobin and deoxyhemoglobin concentrations but also epidermal thickness and melanin concentration along with skin scattering properties. It can detect changes in the diabetic foot and help predict and understand ulceration mechanisms. PMID:20920429
Yudovsky, Dmitry; Nouvong, Aksone; Pilon, Laurent
On September 6, 2008, a Micro-satellite Constellation for Monitoring and Forecasting Environment and Disaster was successfully launched in China. This Micro-satellite Constellation includes two small satellites, Satellite-A (abbreviated as HJ-1A) and Satellite-B (abbreviated as HJ-1B). HJ-1A is installed with a Hyperspectral Imager (abbreviated as HSI), which is China's first satellite-based hyperspectral remote sensor. The advantages of HJ-1A HSI is that
Qiao Wang; Junsheng Li; Qian Shen; Chuanqing Wu; Jianlin Yu
Multispectral remote sensors have been traditionally used to map and monitor anthropogenic and environmental changes in the biosphere. While these sensors have proven robust for many applications, they often lack the spectral resolution necessary to differentiate characteristics of the Earth’s surfa...
Hyperspectral imaging is used to characterize the first basaltic breccia from Mars, Northwest Africa 7034. Initial results show the spectral character of NWA 7034 is unlike other SNC meteorites and may be more representative of average martian crust.
Cannon, K. M.; Mustard, J. F.; Agee, C. B.; Wilson, J. H.; Greenberger, R. N.
In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the designed learning algorithm is able to converge to non-orthogonal independent components. This is particularly useful in hyperspectral image analysis since many materials extracted from a hyperspectral image may have similar spectral signatures and may not be orthogonal. The AVIRIS experiments have demonstrated that the proposed ICA provides an effective unsupervised technique for hyperspectral image classification.
S. S. Chiang; I. W. Ginsberg
Hyperspectral image compression has received considerable interest in recent years due to the enormous data volumes collected by imaging spectrometers for Earth Observation. JPEG2000 is an important technique for data compression which has been successfully used in the context of hyperspectral image compression, either in lossless and lossy fashion. Due to the increasing spatial, spectral and temporal resolution of remotely sensed hyperspectral data sets, fast (onboard) compression of hyperspectral data is becoming a very important and challenging objective, with the potential to reduce the limitations in the downlink connection between the Earth Observation platform and the receiving ground stations on Earth. For this purpose, implementation of hyperspectral image compression algorithms on specialized hardware devices are currently being investigated. In this paper, we develop an implementation of the JPEG2000 compression standard in commodity graphics processing units (GPUs). These hardware accelerators are characterized by their low cost and weight, and can bridge the gap towards on-board processing of remotely sensed hyperspectral data. Specifically, we develop GPU implementations of the lossless and lossy modes of JPEG2000. For the lossy mode, we investigate the utility of the compressed hyperspectral images for different compression ratios, using a standard technique for hyperspectral data exploitation such as spectral unmixing. In all cases, we investigate the speedups that can be gained by using the GPU implementations with regards to the serial implementations. Our study reveals that GPUs represent a source of computational power that is both accessible and applicable to obtaining compression results in valid response times in information extraction applications from remotely sensed hyperspectral imagery.
Ciznicki, Milosz; Kurowski, Krzysztof; Plaza, Antonio
Airborne hyperspectral imaging (HSI) was assessed as a potential tool to locate single grave sites. While airborne HSI has shown to be useful to locate mass graves, it is expected the location of single graves would be an order of magnitude more difficult due to the smaller size and reduced mass of the targets. Two clearings were evaluated (through a blind test) as potential sites for containing at least one set of buried remains. At no time prior to submitting the locations of the potential burial sites from the HSI were the actual locations of the sites released or shared with anyone from the analysis team. The two HSI sensors onboard the aircraft span the range of 408-2524nm. A range of indicators that exploit the narrow spectral and spatial resolutions of the two complimentary HSI sensors onboard the aircraft were calculated. Based on the co-occurrence of anomalous pixels within the expected range of the indicators three potential areas conforming to our underlying assumptions of the expected spectral responses (and spatial area) were determined. After submission of the predicted burial locations it was revealed that two of the targets were located within GPS error (10m) of the true burial locations. Furthermore, due to the history of the TPOF site for burial work, investigation of the third target is being considered in the near future. The results clearly demonstrate promise for hyperspectral imaging to aid in the detection of buried remains, however further work is required before these results can justifiably be used in routine scenarios. PMID:25447169
Leblanc, G; Kalacska, M; Soffer, R
The hyperspectral remote sensing makes use of spectrum resolution with the nano-scale collecting image data simultaneously in dozens or hundreds of narrow and adjacent spectral bands above the earth's surface. These hyperspectral remote sensors make it possible to derive a continuous spectrum line for each image pixel (or a special sort of material). It can acquire space information, radiated information and spectrum information of images synchronously, so that it has remarkable application value and extensive development prospect in many related fields. However, the hyperspectral remote sensing images' characteristics, such as hundreds of bands, high spectral resolution and large volumes of data, have induced many problems such as high ratio of redundant information, large-scale storage space query, long processing delay, the Hughes phenomenon and so on. The main approach to solve these problems is making dimensional reduction before the classification or visual interpretation with the hyperspectral image data. There are two main methods for dimensional reduction: feature abstraction and bands selection. Although the feature abstraction that can achieve the purpose of dimensional reduction, in the process of feature abstraction or non-linear changes in both linear transformation, it will cause the loss of the physical implication of the original image data and also make it hard to apply hyperspectral images to visual interpretation. In contrast, band selection method outperforms in terms of being more universal for application. The selected bands can not only be used as attributes (features) for classification but also synthesize RGB false color image for visual interpretation. Therefore, band selection of hyperspectral remote sensing images is an important dimensional reduction method. Here, we design a hyperspectral remote sensing image band selection algorithm based on differential evolution algorithm. Differential evolution is an evolutionary method based on the idea of recombining different individuals. Evolutionary approach imitates the natural evolution in order to optimize the parameters. Differential evolution method illustrates individuals with floating-point vectors, and processes simple operations to search the optimal solution by natural selection. We employ hyperspectral remote sensing images of 701 uranium deposit (EO1H1320332005197110PY) in the areas of Gansu, China. We compared our new method with the traditional bands selection ways such as genetic algorithm, exhaustive search and steepest rise methods. The experiment results show that the new algorithm can improve the efficiency and stability of the band selection algorithm.
Cai, Z.; Li, Z.; Jiang, A.; Chen, X.
selection algo- rithm. 1. Introduction The development of image sensor technology has made it possibleBoosted Band Ratio Feature Selection for Hyperspectral Image Classification Zhouyu Fu Terry Caelli algorithm for the automatic selection of ratios directly from data. First, a robust method is used
indispensable tools. For acquiring useful images there has been extensive work on imaging through turbulence [1JOINT BLIND DECONVOLUTION AND SPECTRAL UNMIXING OF HYPERSPECTRAL IMAGES Qiang Zhang Dept Science, Wake Forest University, Winston-Salem, NC 27109 Our interest here is spectral imaging for space
Plemmons, Robert J.
Abstract: Hyperspectral image is a sequence of images generated by hundreds of detectors. Each detector is sensitive only to a narrow range of wavelengths. One can view such an image sequence as a three-dimensional array of intensity values (pixels) within a rectangular prism. This image prism reveals contiguous spectrum information about the composition of the area being viewed by the
Xaoli Tang; William A. Pearlman; James W. Modestino
Morphological Component Analysis: From Images to Hyperspectral Data J.-L. Starck CEA, Service d representations Â· Sparsity Â· Why do we need sparsity? Â data compression Â Feature extraction, detection Â Image) Simulated image (gaussians+lines) b) Simulated image + noise c) A trous algorithm d) Curvelet transform e
Improved scanners to be incorporated into hyperspectral microscope-based imaging systems have been invented. Heretofore, in microscopic imaging, including spectral imaging, it has been customary to either move the specimen relative to the optical assembly that includes the microscope or else move the entire assembly relative to the specimen. It becomes extremely difficult to control such scanning when submicron translation increments are required, because the high magnification of the microscope enlarges all movements in the specimen image on the focal plane. To overcome this difficulty, in a system based on this invention, no attempt would be made to move either the specimen or the optical assembly. Instead, an objective lens would be moved within the assembly so as to cause translation of the image at the focal plane: the effect would be equivalent to scanning in the focal plane. The upper part of the figure depicts a generic proposed microscope-based hyperspectral imaging system incorporating the invention. The optical assembly of this system would include an objective lens (normally, a microscope objective lens) and a charge-coupled-device (CCD) camera. The objective lens would be mounted on a servomotor-driven translation stage, which would be capable of moving the lens in precisely controlled increments, relative to the camera, parallel to the focal-plane scan axis. The output of the CCD camera would be digitized and fed to a frame grabber in a computer. The computer would store the frame-grabber output for subsequent viewing and/or processing of images. The computer would contain a position-control interface board, through which it would control the servomotor. There are several versions of the invention. An essential feature common to all versions is that the stationary optical subassembly containing the camera would also contain a spatial window, at the focal plane of the objective lens, that would pass only a selected portion of the image. In one version, the window would be a slit, the CCD would contain a one-dimensional array of pixels, and the objective lens would be moved along an axis perpendicular to the slit to spatially scan the image of the specimen in pushbroom fashion. The image built up by scanning in this case would be an ordinary (non-spectral) image. In another version, the optics of which are depicted in the lower part of the figure, the spatial window would be a slit, the CCD would contain a two-dimensional array of pixels, the slit image would be refocused onto the CCD by a relay-lens pair consisting of a collimating and a focusing lens, and a prism-gratingprism optical spectrometer would be placed between the collimating and focusing lenses. Consequently, the image on the CCD would be spatially resolved along the slit axis and spectrally resolved along the axis perpendicular to the slit. As in the first-mentioned version, the objective lens would be moved along an axis perpendicular to the slit to spatially scan the image of the specimen in pushbroom fashion.
Abstract Initially, Raman spectroscopy was a specialized technique used by vibrational spectroscopists; however, due to rapid advancements in instrumentation and imaging techniques over the last few decades, Raman spectrometers are widely available at many institutions, allowing Raman spectroscopy to become a widespread analytical tool in mineralogy and other geological sciences. Hyperspectral imaging, in particular, has become popular due to the fact that Raman spectroscopy can quickly delineate crystallographic and compositional differences in 2-D and 3-D at the micron scale. Although this rapid growth of applications to the Earth sciences has provided great insight across the geological sciences, the ease of application as the instruments become increasingly automated combined with nonspecialists using this techique has resulted in the propagation of errors and misunderstandings throughout the field. For example, the literature now includes misassigned vibration modes, inappropriate spectral processing techniques, confocal depth of laser penetration incorrectly estimated into opaque crystalline solids, and a misconstrued understanding of the anisotropic nature of sp2 carbons. Key Words: Raman spectroscopy—Raman imaging—Confocal Raman spectroscopy—Disordered sp2 carbons—Hematite—Microfossils. Astrobiology 13, 920–931. PMID:24088070
Olcott Marshall, Alison
We explore the non-Gaussianity of hyperspectral data and present probability models that capture variability of hyperspectral images. In particular, we present a nonparametric probability distribution that models the distribution of the hyperspectral data after reducing the dimension of the data via principal components or Fisher's discriminant analysis. We also explore the directional differences in observed images and present two parametric distributions, the generalized Laplacian and the Bessel K form, that well model the non-Gaussian behavior of directional differences. We then propose a model that labels each spatial site, using Bayesian inference and Markov random fields, that incorporates the information of the nonparametric distribution of the data and the parametric distributions of the directional differences, along with a prior distribution that favors smooth labeling. We then test our model on actual hyperspectral data and present the results of our model, using the Washington D.C. Mall and Indian Springs rural area data sets.
Neher, Robert E., Jr.
Hyperspectral imaging technology is the foreland of the remote sensing development in the 21st century and is one of the most important focuses of the remote sensing domain. Hyperspectral images can provide much more information than multispectral images do and can solve many problems which can't be solved by multispectral imaging technology. However this advantage is at the cost of massy quantity of data that brings difficulties of images' process, storage and transmission. Research on hyperspectral image compression method has important practical significance. This paper intends to do some improvement of the famous KLT-WT-2DSPECK (Karhunen-Loeve transform+ wavelet transformation+ two-dimensional set partitioning embedded block compression) algorithm and advances KLT + bands combination 2DWT + 2DSPECK algorithm. Experiment proves that this method is effective.
Wang, Wenjie; Zhao, Zhongming; Zhu, Haiqing
Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in conjunction with JPEG2000 for hyperspectral-image com- pression. However, the computational cost of determining the data-dependent PCA transform is high due to its traditional eigendecomposition implementation which requires calculation of a covariance matrix across the data. Several strategies
Qian Du; James E. Fowler
A new 2D hyperspectral frame camera system has been developed by VTT (Technical Research Center of Finland) and Rikola Ltd. It contains frame based and very light camera with RGB-NIR sensor and it is suitable for light weight and cost effective UAV planes. MosaicMill Ltd. has converted the camera data into proper format for photogrammetric processing, and camera's geometrical accuracy and stability are evaluated to guarantee required accuracies for end user applications. MosaicMill Ltd. has also applied its' EnsoMOSAIC technology to process hyperspectral data into orthomosaics. This article describes the main steps and results on applying hyperspectral sensor in orthomosaicking. The most promising results as well as challenges in agriculture and forestry are also described.
Mäkeläinen, A.; Saari, H.; Hippi, I.; Sarkeala, J.; Soukkamäki, J.
Persistent surveillance and collection of airborne intelligence, surveillance and reconnaissance information is critical in today's warfare against terrorism. High resolution imagery in visible and infrared bands provides valuable detection capabilities based on target shapes and temperatures. However, the spectral resolution provided by a hyperspectral imager adds a spectral dimension to the measurements, leading to additional tools for detection and identification of targets, based on their spectral signature. The Telops Hyper-Cam sensor is an interferometer-based imaging system that enables the spatial and spectral analysis of targets using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320×256 pixels at spectral resolutions as fine as 0.25 cm-1. The LWIR version covers the 8.0 to 11.8 ?m spectral range. The Hyper-Cam has been recently used for the first time in two compact airborne platforms: a belly-mounted gyro-stabilized platform and a gyro-stabilized gimbal ball. Both platforms are described in this paper, and successful results of high-altitude detection and identification of targets, including industrial plumes, and chemical spills are presented.
Puckrin, Eldon; Turcotte, Caroline S.; Gagnon, Marc-André; Bastedo, John; Farley, Vincent; Chamberland, Martin
Persistent surveillance and collection of airborne intelligence, surveillance and reconnaissance information is critical in today's warfare against terrorism. High resolution imagery in visible and infrared bands provides valuable detection capabilities based on target shapes and temperatures. However, the spectral resolution provided by a hyperspectral imager adds a spectral dimension to the measurements, leading to additional tools for detection and identification of targets, based on their spectral signature. The Telops Hyper-Cam sensor is an interferometer-based imaging system that enables the spatial and spectral analysis of targets using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320×256 pixels at spectral resolutions as fine as 0.25 cm-1. The LWIR version covers the 8.0 to 11.8 ?m spectral range. The Hyper-Cam has been recently used for the first time in two compact airborne platforms: a bellymounted gyro-stabilized platform and a gyro-stabilized gimbal ball. Both platforms are described in this paper, and successful results of high-altitude detection and identification of targets, including industrial plumes, and chemical spills are presented.
Lagueux, Philippe; Puckrin, Eldon; Turcotte, Caroline S.; Gagnon, Marc-André; Bastedo, John; Farley, Vincent; Chamberland, Martin
Hyperspectral remote sensing has been widely utilized in high-resolution climate observation, environment monitoring, resource mapping, etc. However, it brings undesirable difficulties for transmission and storage due to the huge amount of the data. Lossless compression has been demonstrated to be an efficient strategy to solve these problems. In this paper, a novel Band Regrouping based Lossless Compression (BRLlC) algorithm is proposed for lossless compression of hyperspectral images. The affinity propagation clustering algorithm, which can achieve adaptive clustering with high efficiency, is firstly applied to classify all of the hyperspectral bands into several groups based on the inter-band correlation matrix of hyperspectral images. Consequently, hyperspectral bands with high correlation are clustered into one group so that the prediction efficiency in each group can be greatly enhanced. In addition, a linear prediction algorithm based on context prediction is applied to the hyperspectral images in each group followed by arithmetic coding. Experimental results demonstrate that the proposed algorithm outperforms some classic lossless compression algorithms in terms of bit per pixel per band and in terms of processing performance.
He, Mingyi; Bai, Lin; Dai, Yuchao; Zhang, Jing
A hyperspectral fluorescence lifetime imaging (FLIM) instrument is developed to study endogenous fluorophores in biological tissue as an optical biopsy tool. This instrument is able to spectrally, temporally, and spatially resolve fluorescence signal, thus providing multidimensional information to assist clinical tissue diagnosis. An acousto-optic tunable filter (AOTF) is used to realize rapid wavelength switch, and a photomultiplier tube and a high-speed digitizer are used to collect the time-resolved fluorescence decay at each wavelength in real time. The performance of this instrument has been characterized and validated on fluorescence tissue phantoms and fresh porcine skin specimens. This dual-arm AOTF design achieves high spectral throughput while allowing microsecond nonsequential, random wavelength switching, which is highly desirable for time-critical applications. In the results reported here, a motorized scanning stage is used to realize spatial scanning for two-dimensional images, while a rapid beam steering technique is feasible and being developed in an ongoing project. PMID:24002188
Nie, Zhaojun; An, Ran; Hayward, Joseph E; Farrell, Thomas J; Fang, Qiyin
Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spatial and spectral dimensions. We utilize a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) CS model to simulate CS measurements from HyMap images. Flake et al's novel reconstruction algorithm, which combines a spectral smoothing parameter and spatial total variation (TV), is used to create high resolution hyperspectral imagery.1 We examine the e ect of the number of measurements, which corresponds to the percentage of physical data sampled, on the delity of simulated data. The impacts of the CS sensor model and reconstruction of the data cloud and the utility for various hyperspectral applications are described to identify the strengths and limitations of CS.
Busuioceanu, Maria; Messinger, David W.; Greer, John B.; Flake, J. Christopher
Korla fragrant pears are small oval pears characterized by light green skin, crisp texture, and a pleasant perfume for which they are named. Anatomically, the calyx of a fragrant pear may be either persistent or deciduous; the deciduouscalyx fruits are considered more desirable due to taste and texture attributes. Chinese packaging standards require that packed cases of fragrant pears contain 5% or less of the persistent-calyx type. Near-infrared hyperspectral imaging was investigated as a potential means for automated sorting of pears according to calyx type. Hyperspectral images spanning the 992-1681 nm region were acquired using an EMCCD-based laboratory line-scan imaging system. Analysis of the hyperspectral images was performed to select wavebands useful for identifying persistent-calyx fruits and for identifying deciduous-calyx fruits. Based on the selected wavebands, an image-processing algorithm was developed that targets automated classification of Korla fragrant pears into the two categories for packaging purposes.
Rao, Xiuqin; Yang, Chun-Chieh; Ying, Yibin; Kim, Moon S.; Chao, Kuanglin
A new hyperspectral image compression algorithm-NMST (Near Min Spanning Tree) is proposed. The near minimum spanning tree is constructed according to the image structure and is taken as a prediction tree in image compression. The result shows the NMST algorithm can improve the compression speed with little decrease of compression ratio.
HengShu Liu; LianQing Huang
Hyperspectral imaging is of interest in a large number of remote sensing applications, such as geology and pollution monitoring, in order to detect and analyze surface and atmospheric composition. The processing of these images, called spectral analysis, allows for the identification of the specific mineralogical and agricultural elements which compose an image. We seek to understand how loss due to
Kameron Romines; Edwin S. Hong
In this paper, we present an efficient and novel approach to embed hyperspectral imaging (HSI) capability in an intelligent panoramic scanning system for real-time target tracking and signature acquisition. The sensor platform we propose consists of a dual-panoramic peripheral vision component and a narrow field-of-view (FOV) HSI component. The panoramic HSI design optimizes the tradeoff of a wide FOV, a
Tao Wang; Zhigang Zhu; Erik Blasch
A remote-sensing campaign was performed in September 2001 at nighttime under clear-sky conditions before moonrise to assess the level of light pollution of urban and industrial origin. Two hyperspectral sensors, namely, the Multispectral Infrared and Visible Imaging Spectrometer and the Visible Infrared Scanner-200, which provide spectral coverage from the visible to the thermal infrared, were flown over the Tuscany coast
Alessandro Barducci; Paolo Marcoionni; Ivan Pippi; Marco Poggesi
Kernel Based Subspace Projection of Near Infrared Hyperspectral Images of Maize Kernels Rasmus@space.dtu.dk Abstract. In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize of hyperspectral images of maize kernels. 2 Data Acquisition A hyperspectral line-scan NIR camera from Headwall
AN OPERATIONAL APPROACH FOR HYPERSPECTRAL IMAGE COMPRESSION Qian Du, Nam Ly, James E Fowler. Index Terms -- hyperspectral image, compression, classification, anomaly detection. 1. INTRODUCTION superior rate-distortion performance for hyperspectral image compression. In such PCA+JPEG2000 coding, PCA
Fowler, James E.
algorithms in conventional multi-spectral images cannot be used with hyperspectral formats due1 CHAPTER 8 SYSTOLIC S.O.M. NEURAL NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION MartÃnez P Universitario s/n 10071 CÃ¡ceres, Spain E-mail: pablomar,paguilar,rosapere,aplaza @unex.es Hyperspectral image
Plaza, Antonio J.
IMPROVING THE SCALABILITY OF PARALLEL ALGORITHMS FOR HYPERSPECTRAL IMAGE ANALYSIS USING ADAPTIVE on the utilization of lossy data compression techniques for improving the scalability of parallel hyperspectral imag for hyperspectral image anal- ysis is affected by the amount of data to exchanged through the communication network
Plaza, Antonio J.
As 3D images, hyperspectral images result in large sized data sets. The storage and transmission of large volumes of hyperspectral data have become significant concerns. Therefore efficient compression is required for storage and transmission. In this paper, a new hyperspectral remote sensing image compression method based on asymmetric 3D wavelet transform and 3D set partitioning scheme is proposed. Because most
WU Jia-Ji; WU Zhen-Sen; WU Cheng-Ke
preprocessing method which can be combined with any endmember extraction algorithm from hyperspectral imagesJoint Spectral and Spatial Preprocessing Prior to Endmember Extraction from Hyperspectral Images mixed pixel of the original hyperspectral image, where mixed pixels arise due to insufficient spatial
Plaza, Antonio J.
In this paper, a new algorithm for lossless compression of hyperspectral images is proposed. The spectral redundancy in hyperspectral images is exploited using a context-match method driven by the correlation between adjacent bands. This method is suitable for hyperspectral images in the band-sequential format. Moreover, this method compares favorably with the recent proposed lossless compression algorithms in terms of compression,
Hongqiang Wang; S. Derin Babacan; Khalid Sayood
an algorithm for lossy compression of hyperspectral images for imple- mentation on field programmable gateReduced Complexity Wavelet-Based Predictive Coding of Hyperspectral Images for FPGA Implementation collects and stores large amounts of hyperspectral data. For example, one Moderate Resolution Imaging
Spectral/Spatial Hyperspectral Image Compression in Conjunction with Virtual Dimensionality Bharath Abstract Hyperspectral image compression can be performed by either 3-D compression or spectral/spatial compression. It has been demonstrated that due to high spectral resolution hyperspectral image compression can
Plaza, Antonio J.
Prediction algorithms play an important role in lossless compression of hyperspectral images. However, conventional lossless compression algorithms based on prediction are usually inefficient in exploiting correlation in hyperspectral images. In this paper, a new algorithm for lossless compression of hyperspectral images based on 3D context prediction is proposed. The proposed algorithm consists of three parts to exploit the high spectral
Lin Bai; Mingyi He; Yuchao Dai
Cervical cancer is a prevalent disease in many developing countries. Colposcopy is the most common approach for screening cervical intraepithelial neoplasia (CIN). However, its clinical efficacy heavily relies on the examiner's experience. Spectroscopy is a potentially effective method for noninvasive diagnosis of cervical neoplasia. In this paper, we introduce a hyperspectral imaging technique for noninvasive detection and quantitative analysis of cervical neoplasia. A hyperspectral camera is used to collect the reflectance images of the entire cervix under xenon lamp illumination, followed by standard colposcopy examination and cervical tissue biopsy at both normal and abnormal sites in different quadrants. The collected reflectance data are calibrated and the hyperspectral signals are extracted. Further spectral analysis and image processing works are carried out to classify tissue into different types based on the spectral characteristics at different stages of cervical intraepithelial neoplasia. The hyperspectral camera is also coupled with a lab microscope to acquire the hyperspectral transmittance images of the pathological slides. The in vivo and the in vitro imaging results are compared with clinical findings to assess the accuracy and efficacy of the method.
Wang, Chaojian; Zheng, Wenli; Bu, Yanggao; Chang, Shufang; Tong, Qingping; Zhang, Shiwu; Xu, Ronald X.
The use of high resolution surveying techniques has increased dramatically in earth science applications over the last decade. New products, software solutions and an increased attention to "usability" have made terrestrial laser scanning (lidar) and digital photogrammetry popular methods for obtaining more detailed geometric data for many applications. Geology, especially the study of outcrops, is one such application area where the introduction of laser scanning in particular has benefitted, by allowing an increasingly quantitative approach at a variety of scales. Despite this, most of the contribution of modern surveying techniques has been related to the capture of topographic detail - the shape and form of outcrops - while the remote mapping of outcrop lithology has yet to be satisfactorily addressed. Ground-based spectral imaging offers new possibilities for an improved understanding of outcrop composition, by mapping lithology and the distribution of mineralogy with high resolution and increased automation. Advances in airborne and spaceborne multispectral and hyperspectral sensors have been successful for mineral prospecting and the regional mapping of rock types. However, because of the nadir viewing angle of the sensor, such a configuration is of limited value for near-vertical cliff sections. A new generation of close range hyperspectral imagers is now becoming available, with capabilities of measuring in the short-wave infra-red (SWIR) part of the electromagnetic spectrum suitable for detecting absorption features exhibited by many minerals found in sedimentary rocks. This research uses a ground-based hyperspectral sensor to acquire spectral images of geological outcrops, with the aim of remotely determining the distribution of lithologies. The method was applied to case studies from carbonate and siliciclastic rocks. The images were processed to obtain spectral classification maps of the distribution of representative rock types. To increase the quantitative approach, the spectral data were integrated with photorealistic 3D models derived from terrestrial laser scanning and conventional image acquisition. Because the push-broom hyperspectral sensor recorded panoramic rather than planar images, the integration was performed using a cylindrical camera model. Using this approach, it was possible to relate the pixels of the spectral images to a real-world coordinate system, aiding analysis and validation. In addition, the spectral images could be superimposed on the lidar-derived photorealistic models, allowing a simultaneous visualisation of multiple thematic results together with the conventional digital camera imagery. For the case studies used, encouraging results were produced, allowing the mapping of features that were not easily visible in conventional images. It is therefore concluded that ground-based hyperspectral imaging is an important method that may be applicable to many earth science applications.
Kurz, Tobias; Buckley, Simon; Schneider, Danilo; Howell, John
The potential of hyperspectral imaging technology was evaluated for discriminating three types of waxed apples. Three types of apples smeared with fruit wax, with industrial wax, and not waxed respectively were imaged by a hyperspectral imaging system with a spectral range of 308-1 024 nm. ENVI software processing platform was used for extracting hyperspectral image object of diffuse reflection spectral response characteristics. Eighty four of 126 apple samples were selected randomly as calibration set and the rest were prediction set. After different preprocess, the related mathematical models were established by using the partial least squares (PLS), the least squares support vector machine (LS-SVM) and BP neural network methods and so on. The results showed that the model of MSC-SPA-LSSVM was the best to discriminate three kinds of waxed apples with 100%, 100% and 92.86% correct prediction respectively. PMID:24059202
Gao, Jun-Feng; Zhang, Hai-Liang; Kong, Wen-Wen; He, Yong
The forest biome is vital to the health of the earth. Canada and the United States have a combined forest area of 4.68 Mkm2. The monitoring of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of improved information products to land managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory (major forest species), forest health, foliar biochemistry, biomass, and aboveground carbon. Operationally there is a requirement for a mix of airborne and satellite approaches. This paper surveys some methods and results in hyperspectral sensing of forests and discusses the implications for space initiatives with hyperspectral sensing
Goodenough, David G.; Bannon, David
We investigated a lab-based hyperspectral imaging system's response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system's performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert's law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements. PMID:24451451
Mehrubeoglu, Mehrube; Teng, Ming Y.; Zimba, Paul V.
Affordable, long-wave infrared hyperspectral imaging calls for use of an uncooled FPA with high-throughput optics. This paper describes the design of the optical part of a stationary hyperspectral imager in a spectral range of 7-14 um with a field of view of 20°×10°. The imager employs a push-broom method made by a scanning mirror. High throughput and a demand for simplicity and rigidity led to a fully refractive design with highly aspheric surfaces and off-axis positioning of the detector array. The design was optimized to exploit the machinability of infrared materials by the SPDT method and a simple assemblage.
Václavík, Jan; Melich, Radek; Pintr, Pavel; Pleštil, Jan
Coral reefs are one of the most diverse and threatened ecosystems in the world. Corals worldwide are at risk, and in many instances, dying due to factors that affect their environment resulting in deteriorating environmental conditions. Because corals respond quickly to the quality of the environment that surrounds them, corals have been identified as bioindicators of water quality and marine environmental health. The hyperspectral imaging system is proposed as a noninvasive tool to monitor different species of corals as well as coral state over time. This in turn can be used as a quick and non-invasive method to monitor environmental health that can later be extended to climate conditions. In this project, a laboratory-based hyperspectral imaging system is used to collect spectral and spatial information of corals. In the work presented here, MATLAB and ENVI software tools are used to view and process spatial information and coral spectral signatures to identify differences among the coral data. The results support the hypothesis that hyperspectral properties of corals vary among different coral species, and coral state over time, and hyperspectral imaging can be a used as a tool to document changes in coral species and state.
Mehrubeoglu, Mehrube; Smith, Dustin K.; Smith, Shane W.; Strychar, Kevin B.; McLauchlan, Lifford
Lighting for machine vision and hyperspectral imaging is an important component for collecting high quality imagery. However, it is often given minimal consideration in the overall design of an imaging system. Tungsten-halogens lamps are the most common source of illumination for broad spectrum appl...
Lattice vector quantization (LVQ) offers substantial reduction in computational load and design complexity due to the lattice regular structure . In this paper, we extended the SPIHT  coding algorithm with lattice vector quantization to code hyperspectral images. In the proposed algorithm, multistage lattice vector quantization (MLVQ) is used to exploit correlations between image slices, while offering successive refinement with
Ying Liu; W. A. Pearlman
HIGH PERFORMANCE COMPUTING FOR HYPERSPECTRAL IMAGE ANALYSIS: PERSPECTIVE AND STATE sensed hyperspectral im- age processing algorithms on multi-computer clusters, hetero- geneous networks-based hyperspectral image processing and a thoughtful perspective of the potential and emerging challenges of applying
Plaza, Antonio J.
of hyperspectral imaging algorithms, this paper develops several fast parallel data processing techniquesCommodity cluster and hardware-based massively parallel implementations of hyperspectral imaging of the proposed algorithms. In order to explore the viability of developing onboard, real-time hyperspectral data
Plaza, Antonio J.
In response to the high data volumes of hyperspectral images and high data rates required for their transmission, many hyperspectral data compression methods have been researched in recent years. In this paper, a new compression algorithm for hyperspectral image is proposed, which pays much more attention to its peculiarity of much higher spectral resolution. The new method combines three-dimensional integer
Shanshan Yu; Yezhang Zhang
The retrieval of impervious surface is a hot topic in the remote sensing field in the past decade. Nevertheless, studies on retrieving impervious surface from hyperspectral image and the comparison of the performances in retrieving impervious surface between hyperspectral and multispectral images are rarely reported. Therefore, The present paper focuses on the characteristics of hyperspectral (EO-1 Hyperion) and multispectral (Landsat TM/ETM+) images and implements a complementary study on the comparison based on the retrieved impervious surface information between Hyperion and TM/ETM+ data. For up to 242 bands of Hyperion image, a further study was carried out to select feature bands for impervious surface retrieving using stepwise discriminant analysis. As a result, 11 feature bands were selected and a new image named Hyperion' was thus composed. The new Hyperion' image was used to investigate whether this band-reduced image could obtain higher accuracy in retrieving impervious surface. The three test regions were selected from Fuzhou, Guangzhou and Hangzhou of China, with date-coincident or nearly coincident image pairs of the used sensors. The linear spectral mixture analysis (LSMA) was employed to retrieve impervious surface and the results were accessed for their accuracy. The comparison shows that the Hyperion image has higher accuracy than TM/ETM+, and the Hyperion' composed of the selected 11 feature bands has the highest accuracy. The advantages of Hyperion in spectral and radiometric resolutions over TM/ETM+ are believed to be the main factors contributing to the higher accuracy. The high spectral and radiometric resolutions of Hyperion image allow the sensor to have higher sensitivity in distinguishing subtle spectral changes of ground objects. While, the highest accuracy the 11-band Hyperion' image achieved is owing to the significant reduction of the band dimension of the image and thus the band redundancy. PMID:25007632
Tang, Fei; Xu, Han-Qiu
wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each2334 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 17, NO. 12, DECEMBER 2008 Hyperspectral Image
Tunable, narrow-wavelength spectral filters with a ms response in the mid-wave/long-wave infrared (MW/LWIR) are an enabling technology for hyperspectral imaging systems. Few commercial off-the-shelf (COTS) components for this application exist, including filter wheels, movable gratings, and Fabry-Perot (FP) etalon-based devices. These devices can be bulky, fragile and often do not have the required response speed. Here, we present a fundamentally different approach for tunable reflective IR filters, based on coupling subwavelength plasmonic antenna arrays with liquid crystals (LCs). Our device operates in reflective mode and derives its narrow bandwidth from diffractive coupling of individual antenna elements. The wavelength tunability of the device arises from electrically-induced re-orientation of the LC material in intimate contact with antenna array. This re-orientation, in turn, induces a change in the local dielectric environment of the antenna array, leading to a wavelength shift. We will first present results of full-field optimization of micron-size antenna geometries to account for complex 3D LC anisotropy. We have fabricated these antenna arrays on IR-transparent CaF2 substrates utilizing electron beam lithography, and have demonstrated tunability using 5CB, a commercially available LC. However, the design can be extended to high-birefringence liquid crystals for an increased tuning range. Our initial results demonstrate <60% peak reflectance in the 4- 6 ?m wavelength range with a tunability of 0.2 ?m with re-orientation of the surface alignment layers. Preliminary electrical switching has been demonstrated and is being optimized.
Liberman, V.; Parameswaran, L.; Gear, C.; Cabral, A.; Rothschild, M.
Previous studies demonstrated a hyperspectral imaging system has a potential for poultry fecal contaminant detection by measuring\\u000a reflectance intensity. The simple image ratio at 565 and 517 nm images with optimal thresholding was able to detect fecal\\u000a contaminants on broiler carcasses with high accuracy. However, differentiating false positives from real contaminants, especially\\u000a cecal feces were challenging. Further image processing such as
Michio Kise; William R. Windham; Kurt C. Lawrence; Seung C. Yoon
IMSS utilizes a very simple optical design that enables a robust and low cost hyperspectral imaging instrument. This technology was developed under a phase II SBIR with the Air Force Philips Lab. (Dr. Paul LeVan), for the midwave IR to perform clutter rejection and target identification based upon IR spectral signatures. The prototype instrument has been field tested on numerous occasions and successfully measured background, aircraft, and misile plume spectra. PAT currently has several contracts to commercialize this technology both for the DoD and the commercial market. Under contract to the BMDO, (Paul McCarley), with matching funds from Amber Engineering, we are developing an F/2.3 system that will be sold by Amber Engineering as an accessory to the Radiance 1 camera. PAT is also under contract with ONR (Mr. Jim Buss), to develop a longwave IR version of IMSS as well as an MWIR version tuned to operate as a 'little sister sensor for target identification' for the Navy's IRST's. The purpose of this paper is to briefly describe the hyperspectral image data that was collected in the field at Long Jump '94, and Santa Ynez Peak using IMSS prototype hyperspectral imager. Examples of spectral images, as well as spectra of different aircraft at various ranges, power settings, and aspect angles, an Atlas liquid hydrocarbon burning missile, and a solid beester. All data presented in this paper are a result of a single spectral scan. The limitation in digital storage of the prototype system do not allow multiple scans in order to improve signal to noise. In spite of this limitation, the performance of the prototype system has proven to be excellent.
Hinnrichs, Michele; Massie, Mark A.
Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.
Algorithms and information technologies for processing Earth hyperspectral imagery are presented. Several new approaches are discussed. Peculiar properties of processing the hyperspectral imagery, such as multifold signal-to-noise reduction, atmospheric distortions, access to spectral characteristics of every image point, and high dimensionality of data, were studied. Different measures of similarity between individual hyperspectral image points and the effect of additive uncorrelated noise on these measures were analyzed. It was shown that these measures are substantially affected by noise, and a new measure free of this disadvantage was proposed. The problem of detecting the observed scene object boundaries, based on comparing the spectral characteristics of image points, is considered. It was shown that contours are processed much better when spectral characteristics are used instead of energy brightness. A statistical approach to the correction of atmospheric distortions, which makes it possible to solve the stated problem based on analysis of a distorted image in contrast to analytical multiparametric models, was proposed. Several algorithms used to integrate spectral zonal images with data from other survey systems, which make it possible to image observed scene objects with a higher quality, are considered. Quality characteristics of hyperspectral data processing were proposed and studied.
Achmetov, R. N.; Stratilatov, N. R.; Yudakov, A. A.; Vezenov, V. I.; Eremeev, V. V.
Abstract. Hyperspectral imaging (HSI) is an emerging modality for various medical applications. Its spectroscopic data might be able to be used to noninvasively detect cancer. Quantitative analysis is often necessary in order to differentiate healthy from diseased tissue. We propose the use of an advanced image processing and classification method in order to analyze hyperspectral image data for prostate cancer detection. The spectral signatures were extracted and evaluated in both cancerous and normal tissue. Least squares support vector machines were developed and evaluated for classifying hyperspectral data in order to enhance the detection of cancer tissue. This method was used to detect prostate cancer in tumor-bearing mice and on pathology slides. Spatially resolved images were created to highlight the differences of the reflectance properties of cancer versus those of normal tissue. Preliminary results with 11 mice showed that the sensitivity and specificity of the hyperspectral image classification method are 92.8% to 2.0% and 96.9% to 1.3%, respectively. Therefore, this imaging method may be able to help physicians to dissect malignant regions with a safe margin and to evaluate the tumor bed after resection. This pilot study may lead to advances in the optical diagnosis of prostate cancer using HSI technology. PMID:22894488
Akbari, Hamed; Halig, Luma V.; Schuster, David M.; Osunkoya, Adeboye; Master, Viraj; Nieh, Peter T.; Chen, Georgia Z.; Fei, Baowei
In this paper, we investigate a low-complexity scheme for decoding compressed hyperspectral image data. We have exploited the simplicity of the subgradient method by modifying a total variation-based regularization problem to include a residual constraint, employing convex optimality conditions to provide equivalency between the original and reformed problem statements. A scheme that utilizes spectral smoothness by calculating informed starting points to improve the rate of convergence is introduced. We conduct numerical experiments, using both synthetic and real hyperspectral data, to demonstrate the effectiveness of the reconstruction algorithm and the validity of our method for exploiting spectral smoothness. Evidence from these experiments suggests that the proposed methods have the potential to improve the quality and run times of the future compressed hyperspectral image reconstructions. PMID:25438319
Eason, Duncan T; Andrews, Mark
A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural features were extracted by volumetric gray-level co-occurrence matrices (VGLCM). The spectral features were extracted by minimum estimated abundance covariance (MEAC) and linear prediction (LP)-based band selection, and a semi-supervised k-means (SKM) clustering method with deleting the worst cluster (SKMd) bandclustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classification by using spectral and textural features. It has been proven that the proposed method using VGLCM outperforms the gray-level co-occurrence matrices (GLCM) method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.
Su, Hongjun; Sheng, Yehua; Du, Peijun; Chen, Chen; Liu, Kui
.2 Target Detection Problem . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2.1 Target Detection . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2.2 Target Detection Of Hyper-spectral Images . . . . . . . . . . . 2 1.3 The Contributions... 2.1 Hyper-Spectral Imagery . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 The Hyper-spectral Data Cube . . . . . . . . . . . . . . . . . 4 2.1.2 Challenges In Hyper-Spectral Image Processing . . . . . . . . 5 2.2 A Brief Introduction...
Since 2009 the Idaho National Lab (INL) has been developing advanced remote sensing capabilities that combine increasingly sophisticated miniaturized sensors with relatively affordable, light weight (under 75 kg) unmanned aerial vehicles (UAVs). UAV-based hyperspectral sensing capabilities have been routinely refined via flight tests conducted at INL's UAV Runway Research Park in southeastern Idaho, and at the Orchard Training Area in central Idaho. Idaho State University (ISU) Boise Center Aerospace Lab (BCAL) has provided field data collection and image processing support to target ground versus aerial data comparisons, assess spectral and geometric data accuracy and determine classification algorithms appropriate for vegetation management applications. We report instrumentation, sensor and image validation results, optimal flight parameters, and methods for improving the geometric accuracies of the datasets. We also assess the accuracy of narrowband vegetation indices and shrub cover estimates derived from the imagery. Preliminary results indicate that the UAV-based hyperspectral imaging system has potential to bridge the gap between costly in-situ data collections, coarse resolution satellite data collections, or infrequent and costly manned hyperspectral data collections. Furthermore, new areas of research may be possible with this UAV platform by providing an affordable, on-demand platform that can rapidly collect transect data and stay on station for hours.
Mitchell, J.; Hruska, R.; Anderson, M.; Glenn, N. F.
A context-modeling sub-algorithm has been developed as part of an algorithm that effects three-dimensional (3D) wavelet-based compression of hyperspectral image data. The context-modeling subalgorithm, hereafter denoted the context modeler, provides estimates of probability distributions of wavelet-transformed data being encoded. These estimates are utilized by an entropy coding subalgorithm that is another major component of the compression algorithm. The estimates make it possible to compress the image data more effectively than would otherwise be possible. The following background discussion is prerequisite to a meaningful summary of the context modeler. This discussion is presented relative to ICER-3D, which is the name attached to a particular compression algorithm and the software that implements it. The ICER-3D software is summarized briefly in the preceding article, ICER-3D Hyperspectral Image Compression Software (NPO-43238). Some aspects of this algorithm were previously described, in a slightly more general context than the ICER-3D software, in "Improving 3D Wavelet-Based Compression of Hyperspectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. In turn, ICER-3D is a product of generalization of ICER, another previously reported algorithm and computer program that can perform both lossless and lossy wavelet-based compression and decompression of gray-scale-image data. In ICER-3D, hyperspectral image data are decomposed using a 3D discrete wavelet transform (DWT). Following wavelet decomposition, mean values are subtracted from spatial planes of spatially low-pass subbands prior to encoding. The resulting data are converted to sign-magnitude form and compressed. In ICER-3D, compression is progressive, in that compressed information is ordered so that as more of the compressed data stream is received, successive reconstructions of the hyperspectral image data are of successively higher overall fidelity.
Kiely, Aaron; Xie, Hua; Klimesh, matthew; Aranki, Nazeeh
Abiotic and disease-induced stress significantly reduces plant productivity. Automated on-the-go mapping of plant stress allows timely intervention and mitigating of the problem before critical thresholds are exceeded, thereby, maximizing productivity. A hyperspectral camera analyzed the spectral ...
According to the imaging principle and characteristic of LASIS (Large Aperture Static Interference Imaging Spectrometer), we discovered that the 3D (three dimensional) image sequences formed by different interference pattern frames, which were formed in the imaging process of LASIS Interference hyperspectral image, had much stronger correlation than the original interference hyperspectral image sequences, either in 2D (two dimensional) spatial domain
Jia Wen; Caiwen Ma; Penglang Shui
The German Aerospace Center's (DLR) Remote Sensing Technology Institute (IMF) operates a laboratory for the characterisation of imaging spectrometers. Originally designed as Calibration Home Base (CHB) for the imaging spectrometer APEX, the laboratory can be used to characterise nearly every airborne hyperspectral system. Characterisation methods will be demonstrated exemplarily with HySpex, an airborne imaging spectrometer system from Norsk Elektro Optikks A/S (NEO). Consisting of two separate devices (VNIR-1600 and SWIR-320me) the setup covers the spectral range from 400 nm to 2500 nm. Both airborne sensors have been characterised at NEO. This includes measurement of spectral and spatial resolution and misregistration, polarisation sensitivity, signal to noise ratios and the radiometric response. The same parameters have been examined at the CHB and were used to validate the NEO measurements. Additionally, the line spread functions (LSF) in across and along track direction and the spectral response functions (SRF) for certain detector pixels were measured. The high degree of lab automation allows the determination of the SRFs and LSFs for a large amount of sampling points. Despite this, the measurement of these functions for every detector element would be too time-consuming as typical detectors have 105 elements. But with enough sampling points it is possible to interpolate the attributes of the remaining pixels. The knowledge of these properties for every detector element allows the quantification of spectral and spatial misregistration (smile and keystone) and a better calibration of airborne data. Further laboratory measurements are used to validate the models for the spectral and spatial properties of the imaging spectrometers. Compared to the future German spaceborne hyperspectral Imager EnMAP, the HySpex sensors have the same or higher spectral and spatial resolution. Therefore, airborne data will be used to prepare for and validate the spaceborne system's data.
Baumgartner, Andreas; Gege, Peter; Köhler, Claas; Lenhard, Karim; Schwarzmaier, Thomas
A hyperspectral imaging system (HsI), described previously, was utilized to evaluate and monitor wounds and their healing surgery and post-operatively. Briefly, the system consists of a DLP® based spectral light modulator providing active spectral illumination that is synchronized with a digital focal plan array for collecting spectroscopic images that are processed for mapping the percentage of oxyhemoglobin at each detector pixel non-invasively and at near video rates ~8 chemically encode images per second.
La Fontaine, Javier; Lavery, Lawrence; Zuzak, Karel
Over the past decade, researchers at the Agricultural Research Service (ARS), United States Department of Agriculture (USDA), have developed several versions of line-scan-based hyperspectral imaging systems capable of both visible to near-infrared reflectance and fluorescence methods. These line-s...
This paper analyzes the feasibility and performance of HSI systems for medical diagnosis as well as for food safety. Illness prevention and early disease detection are key elements for maintaining good health. Health care practitioners worldwide rely on innovative electronic devices to accurately identify disease. Hyperspectral imaging (HSI) is an emerging technique that may provide a less invasive procedure than
Oscar Carrasco; Richard Gomez; Arun Chainani; William Roper
A hyperspectral fluorescence imaging system was developed and used to obtain several two-waveband spectral ratios on leafy green vegetables, represented by romaine lettuce and baby spinach in this study. The ratios were analyzed to determine the proper one for detecting bovine fecal contamination on...
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than 30 years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the highdimensional nature of the data, and to integrate the spatial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific applications, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms
Plaza, Antonio; Benediktsson, Jon Atli; Boardman, Joseph W.; Brazile, Jason; Bruzzone, Lorenzo; Camps-Valls, Gustavo; Chanussot, Jocelyn; Fauvel, Mathieu; Gamba, Paolo; Gualtieri, Anthony; Marconcini, Mattia; Tilton, James C.; Trianni, Giovanna
We present several infrared hyperspectral images acquired from the perspective of a rover or lander, similar to those that will be acquired from the Mars Surveyor 2001, 2003, and 2005 missions. Super-resolution techniques are used to enhance detail in the scenes observed.
Moersch, J. E.; Roush, T. L.; Farmer, J.
A hyperspectral imaging system with optimum classifiers enables us to identify the type and source of various contaminants (duodenum, ceca, colon, and ingesta) to determine critical control point for science-based federal poultry safety inspection program. The classification accuracies varied from ...
Mechanical injury often causes hidden internal damage to pickling cucumbers, which is difficult to detect in visual inspection. Bruised pickling cucumbers lower the quality of pickled products and can incur economic losses to the processor. A near-infrared hyperspectral imaging system was developed ...
feature selection   and decision fusion is proposed. In this method, after a single-level 3D DWT-classifiers and decision fusion systems that are designed to handle the high-dimensional 3D DWT feature spaces. TwoA Multiclassifier and Decision Fusion System for Hyperspectral Image Classification Zhen Ye School
Fowler, James E.
There is an ever-increasing need to be able to detect the presence of explosives, preferably from standoff distances. This paper presents an application of visible hyperspectral imaging using anomaly, polarization and spectral identification approaches for the standoff detection (13 meters) of nitroaromatic explosives on realistic painted surfaces based upon the colorimetric differences between tetryl and TNT which are enhanced by solar irradiation.
Bernacki, Bruce E.; Blake, Thomas A.; Mendoza, Albert; Johnson, Timothy J.
Hyperspectral microscope imaging (HMI) method, which provides both spatial and spectral information, can be effective for foodborne pathogen detection. The acousto-optic tunable filter (AOTF)-based HMI method can be used to characterize spectral properties of biofilms formed by Salmonella enteritidi...
Oxygenation of the facial skin was evaluated in rosacea using a hyperspectral camera. A portable imaging system utilizing crossed-polarization optics for illumination and recording is described. Relative oxygen saturation was determined from rosacea features and compared with normal skin. Saturation maps and light absorption spectra showed a significant increase in the oxygen saturation of the blood in rosacea-affected skin.
Beach, James M.; Lanoue, Mark A.; Brabham, Kori; Khoobehi, Bahram
In this paper, we propose an algorithm for reference bands selection in hyperspectral image (HSI) compression. In HSI compression, many algorithms need to select the reference bands, but all those algorithms uniformly select the reference bands. The uniform selection method isn't optimal, so we propose a genetic algorithm (GA) based reference band selection method to get lower prediction residual. The
Yushi Chen; Aili Wang; Ye Zhang
A hyperspectral fluorescence imaging system was developed and used to obtain several two-waveband spectral ratios on leafy green vegetables, represented by romaine lettuce and baby spinach in this study. The ratios were analyzed to determine the proper one for detecting bovine fecal contamination on...
Molecular imaging is a rapidly growing area of research, fueled by needs in pharmaceutical drug-development for methods for high-throughput screening, pre-clinical and clinical screening for visualizing tumor growth and drug targeting, and a growing number of applications in the molecular biology fields. Small animal fluorescence imaging employs fluorescent probes to target molecular events in vivo, with a large number of molecular targeting probes readily available. The ease at which new targeting compounds can be developed, the short acquisition times, and the low cost (compared to microCT, MRI, or PET) makes fluorescence imaging attractive. However, small animal fluorescence imaging suffers from high optical scattering, absorption, and autofluorescence. Much of these problems can be overcome through multispectral imaging techniques, which collect images at different fluorescence emission wavelengths, followed by analysis, classification, and spectral deconvolution methods to isolate signals from fluorescence emission. We present an alternative to the current method, using hyperspectral excitation scanning (spectral selection imaging), a technique that allows excitation at any wavelength in the visible and near-infrared wavelength range. In many cases, excitation imaging may be more effective at identifying specific fluorescence signals because of the higher complexity of the fluorophore excitation spectrum. Because the excitation is filtered and not the emission, the resolution limit and image shift imposed by acousto-optic tunable filters have no effect on imager performance. We will discuss design of the imager, optimizing the imager for use in small animal fluorescence imaging, and application of spectral analysis and classification methods for identifying specific fluorescence signals.
Leavesley, Silas; Jiang, Yanan; Patsekin, Valery; Hall, Heidi; Vizard, Douglas; Robinson, J. Paul
In this paper a new technology, based on HyperSpectral Imaging (HSI) sensors, and related detection architectures, is investigated in order to develop suitable and low cost strategies addressed to: i) preliminary detection and characterization of the composition of the structure to dismantle and ii) definition and implementation of innovative smart detection engines for sorting and/or demolition waste flow stream quality control. The proposed sensing architecture is fast, accurate, affordable and it can strongly contribute to bring down the economic threshold above which recycling is cost efficient. Investigations have been carried out utilizing an HSI device working in the range 1000-1700 nm: NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Spectral data analysis was carried out utilizing the PLS_Toolbox (Version 6.5.1, Eigenvector Research, Inc.) running inside Matlab® (Version 7.11.1, The Mathworks, Inc.), applying different chemometric techniques, selected depending on the materials under investigation. The developed procedure allows assessing the characteristics, in terms of materials identification, such as recycled aggregates and related contaminants, as resulting from end-of-life concrete processing. A good classification of the different classes of material was obtained, being the model able to distinguish aggregates from other materials (i.e. glass, plastic, tiles, paper, cardboard, wood, brick, gypsum, etc.).
Serranti, Silvia; Bonifazi, Giuseppe
Leafy spurge (Euphoria esula L.) is a perennial noxious weed that has been encroaches on the native grassland regions of North America resulting in biological and economic impacts. Leafy spurge growth is most prevalent along river banks and in pasture areas. Due to poor accessibility and the cost and labour associated with data collection, estimates of number and size of leafy spurge infestations is poor. Remote sensing has the ability to cover large areas, providing an alternate means to ground surveys and will allow for the capability to create an accurate baseline of infestations. Airborne hyperspectral data were collected over the two test sites selected on the Blood Reserve in Southern Alberta using a combined Airborne Imaging Spectrometer for different Applications (AISA) Eagle and Hawk sensor systems in July, 2010. This study used advanced analysis tools, including spectral mixture analysis, spectral angle mapper and mixture-tuned matched filter techniques to evaluate the ability to detect leafy spurge patches. The results show that patches of leafy spurge with flowering stem density >40 stems m-2 were identified with 85 % accuracy while identification of lower density stems were less accurate (10 - 40 %). The results are promising with respect to quantifying areas of significant leafy spurge infestation and targeting biological control and potential insect release sites.
This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance sp...
This article reports on using a moment method to extract features from the hyperspectral scattering profiles for apple fruit firmness prediction. Hyperspectral scattering images between 500 nm and 1000 nm were acquired online, using a hyperspectral scattering system, for ‘Golden Delicious’, ’Jonagol...
Towards Real-Time Compression of Hyperspectral Images Using Virtex-II FPGAs Antonio Plaza to designing and developing compression algorithms for hyperspectral imagery. Unfortu- nately, most available algorithms for hyperspectral imagery . Two types of data compression can be performed, lossless and lossy
Plaza, Antonio J.
The objective of this investigation was to study the use of a new type of a low-weight unmanned aerial vehicle (UAV) imaging system in the precision agriculture. The system consists of a novel Fabry-Perot interferometer based hyperspectral camera and a high-resolution small-format consumer camera. The sensors provide stereoscopic imagery in a 2D frame-format and they both weigh less than 500 g. A processing chain was developed for the production of high density point clouds and hyperspectral reflectance image mosaics (reflectance signatures), which are used as inputs in the agricultural application. We demonstrate the use of this new technology in the biomass estimation process, which is based on support vector regression machine. It was concluded that the central factors influencing on the accuracy of the estimation process were the quality of the image data, the quality of the image processing and digital surface model generation, and the performance of the regressor. In the wider perspective, our investigation showed that very low-weight, low-cost, hyperspectral, stereoscopic and spectrodirectional 3D UAV-remote sensing is now possible. This cutting edge technology is powerful and cost efficient in time-critical, repetitive and locally operated remote sensing applications.
Honkavaara, E.; Kaivosoja, J.; Mäkynen, J.; Pellikka, I.; Pesonen, L.; Saari, H.; Salo, H.; Hakala, T.; Marklelin, L.; Rosnell, T.
Citrus canker is one of the most devastating diseases that threaten citrus crops. Technologies that can efficiently identify citrus canker would assure fruit quality and safety and enhance the competitiveness and profitability of the citrus industry. This research was aimed to investigate the potential of using hyperspectral imaging technique for detecting canker lesions on citrus fruit. A portable hyperspectral imaging system consisting of an automatic sample handling unit, a light source, and a hyperspectral imaging unit was developed for citrus canker detection. The imaging system was used to acquire reflectance images from citrus samples in the wavelength range between 400 nm and 900 nm. Ruby Red grapefruits with normal and various diseased skin conditions including canker, copper burn, greasy spot, wind scar, cake melanose, and specular melanose were tested. Hyperspectral reflectance images were analyzed using principal component analysis (PCA) to compress the 3-D hyperspectral image data and extract useful image features that could be used to discriminate cankerous samples from normal and other diseased samples. Image processing and classification algorithms were developed based upon the transformed images of PCA. The overall accuracy for canker detection was 92.7%. This research demonstrated that hyperspectral imaging technique could be used for discriminating citrus canker from other confounding diseases.
Qin, Jianwei; Burks, Thomas F.; Kim, Moon S.; Chao, Kuanglin; Ritenour, Mark A.
area of the image, high spatial resolution. An adaptation of 3D-SPIHT image compression algorithm or remote sensing hyperspectral images. Due to the huge amount of data involved, even the compressed imagesThree-dimensional SPIHT Coding of Hyperspectral Images with Random Access and Resolution
In this paper, an algorithm for hyperspectral image compression is presented. It carries DCT (Discrete Cosine Transform) on spectral bands to exploit the spectral correlation and then DWT (Discrete Wavelet Transform) on every eigen image to exploit the spatial correlation. After that, 3D-SPIHT (three-dimensional Set Partitioning in Hierarchical Trees) is performed for encoding. Experiments were done on the OMIS-I (Operational
Haiping Wei; Baojun Zhao; Peikun He
In recent few years, DSC (Distributed Source Coding) technology is catched much attentions in remote sensing image compression field,due to its excellent performance and low encoding complexity. In this paper, we propose a DSC-based practical solution for hyperspectral image lossless compression system, which applies the DSC technique using the power channel codes of Low-Density-Parity-Check Accumulated(LDPCA) codes and incorporates an efficient
Xueping Yan; Jiaji Wu
We propose a new lossy compression algorithm for hyperspectral images, which is based on the spectral Karhunen-Loeve transform, followed by spatial JPEG 2000, which employs a model of anomalous pixels during the compression process. Results on Airborne Visible\\/Infrared Imaging Spectrometer scenes show that the new algorithm provides better rate-distortion performance, as well as improved anomaly detection performance, with respect to
Barbara Penna; Tammam Tillo; Enrico Magli; Gabriella Olmo
A novel technique has been developed to obtain simultaneous tomographic images of temperature and species concentration based on hyperspectral absorption spectroscopy. The hyperspectral information enables several key advantages when compared to traditional tomography techniques based on limited spectral information. These advantages include a significant reduction in the number of required projection measurements, and an enhanced insensitivity to measurements/inversion uncertainties. These advantages greatly facilitate the practical implementation and application of the tomography technique. This paper reports the development of the technique, and the experimental demonstration of a prototype sensor in a near-adiabatic, atmospheric-pressure laboratory Hencken burner. The spatial and temporal resolution enabled by this new sensing technique is expected to resolve several key issues in practical combustion devices. PMID:19434193
Ma, Lin; Cai, Weiwei; Caswell, Andrew W; Kraetschmer, Thilo; Sanders, Scott T; Roy, Sukesh; Gord, James R
Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification
Picon, Artzai; Ghita, Ovidiu; Rodriguez-Vaamonde, Sergio; Iriondo, Pedro Ma; Whelan, Paul F.
Optical diagnostics of bruised skin might provide important information for characterization and age determination of such injuries. Hyperspectral imaging is one of the optical techniques that have been employed for bruise characterization. This technique combines high spatial and spectral resolution and makes it possible to study both chromophore signatures and -distributions in an injury. Imaging and spectroscopy in the visible spectral range have resulted in increased knowledge about skin bruises. So far the SWIR region has not been explored for this application. The main objective of the current study was to characterize bruises in the SWIR wavelength range. Hyperspectral images in the SWIR (950-2500nm ) and VNIR (400-850nm) spectral range were collected from 3 adult volunteers with bruises of known age. Data were collected over a period of 8 days. The data were analyzed using spectroscopic techniques and statistical image analysis. Preliminary results from the pilot study indicate that SWIR hyperspectral imaging might be an important supplement to imaging in the visible part of the spectrum. The technique emphasizes local edema and gives a possibility to visualize features that cannot easily be seen in the visible part of the spectrum.
Randeberg, Lise L.; Hernandez-Palacios, Julio
Dental caries is a disease characterized by demineralization of enamel crystals leading to the penetration of bacteria into the dentine and pulp. Early detection of enamel demineralization resulting in increased enamel porosity, commonly known as white spots, is a difficult diagnostic task. Laser induced autofluorescence was shown to be a useful method for early detection of demineralization. The existing studies involved either a single point spectroscopic measurements or imaging at a single spectral band. In the case of spectroscopic measurements, very little or no spatial information is acquired and the measured autofluorescence signal strongly depends on the position and orientation of the probe. On the other hand, single-band spectral imaging can be substantially affected by local spectral artefacts. Such effects can significantly interfere with automated methods for detection of early caries lesions. In contrast, hyperspectral imaging effectively combines the spatial information of imaging methods with the spectral information of spectroscopic methods providing excellent basis for development of robust and reliable algorithms for automated classification and analysis of hard dental tissues. In this paper, we employ 405 nm laser excitation of natural caries lesions. The fluorescence signal is acquired by a state-of-the-art hyperspectral imaging system consisting of a high-resolution acousto-optic tunable filter (AOTF) and a highly sensitive Scientific CMOS camera in the spectral range from 550 nm to 800 nm. The results are compared to the contrast obtained by near-infrared hyperspectral imaging technique employed in the existing studies on early detection of dental caries.
Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan
Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km(2) of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km(2)). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas. PMID:23922921
Kobryn, Halina T; Wouters, Kristin; Beckley, Lynnath E; Heege, Thomas
Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km2 of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km2). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas. PMID:23922921
Kobryn, Halina T.; Wouters, Kristin; Beckley, Lynnath E.; Heege, Thomas
Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images.
Pike, Robert; Patton, Samuel K.; Lu, Guolan; Halig, Luma V.; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei
We describe and validate an automated methodology based on PPI to extract endmembers from images and distinct the according endmembers. Four main steps are:1)project the raw image cube to its most spectral dimensions and non-noise components by minimum noise fraction (MNF) technology; 2) use the set of spectrally distinct pixels produced by MNF as skewers for PPI, generates a list of candidates from which final endmembers can be selected; 3) an automatic selection procedure based on K-means clustering is consequently performed to determined the centriod of endmenbers. 4) linear spectral mixing model (LSMM) is used to estimate mixing coefficient. And root mean square error (RMSE) reflects the accuracy of decomposition. We use the methodology to investigate the unique properties of hyperspectral data and how spectral information can be used to identify mineralogy with the Airborne Visible/infrared imaging Spectrometer (AVIRIS) hyperspectral data from Cuprite, Nevada.
Wang, Wenyu; Cai, Guoyin
Multi-spectral and hyperspectral image data payloads have large size and may be challenging to download from remote sensors. To alleviate this problem, such images can be effectively compressed using specially designed algorithms. The new CCSDS-123 standard has been developed to address onboard lossless coding of multi-spectral and hyperspectral images. The standard is based on the fast lossless algorithm, which is composed of a causal context-based prediction stage and an entropy-coding stage that utilizes Golomb power-of-two codes. Several parts of each of these two stages have adjustable parameters. CCSDS-123 provides satisfactory performance for a wide set of imagery acquired by various sensors; but end-users of a CCSDS-123 implementation may require assistance to select a suitable combination of parameters for a specific application scenario. To assist end-users, this paper investigates the performance of CCSDS-123 under different parameter combinations and addresses the selection of an adequate combination given a specific sensor. Experimental results suggest that prediction parameters have a greater impact on the compression performance than entropy-coding parameters.
Augé, Estanislau; Sánchez, Jose Enrique; Kiely, Aaron; Blanes, Ian; Serra-Sagristà, Joan
In this paper we present a new lossless algorithm for compression of the signals from advanced hyperspectral infrared sensors onboard sun- and geo-synchronous environmental satellites. At each stage, our compression algorithm achieves an efficient grouping of channel data points around a relatively small number of 1-dimensional lines in a large dimensional data space. The parametrization of these lines is very efficient, which leads to efficient descriptions of data points via adaptive clustering. Using one full day's worth (24 h) of global hyperspectral data obtained by the AQUA-EOS Atmospheric Infrared Sounder (AIRS), the mean ratio of compression attainable by the method is shown to be ? 3.7 to 1.
Gladkova, I.; Nalli, N.; Wolf, W.; Zhou, L.; Goldberg, M.; Roytman, L.
Tenderness is a primary determinant of consumer satisfaction of beef steaks. The objective of this study was to implement\\u000a and test near-infrared (NIR) hyperspectral imaging to forecast 14-day aged, cooked beef tenderness from the hyperspectral\\u000a images of fresh ribeye steaks (n = 319) acquired at 3–5 day post-mortem. A pushbroom hyperspectral imaging system (wavelength range: 900–1700 nm) with a diffuse-flood
Govindarajan Konda Naganathan; Lauren M. Grimes; Jeyamkondan Subbiah; Chris R. Calkins; Ashok Samal; George E. Meyer
Hyperspectral instruments are capable of collecting hundreds of images corresponding to wavelength channels for the same area on the earth surface. Due to the huge number of features (bands) in hyperspectral imagery, land cover classification procedures are computationally expensive and pose a problem known as the curse of dimensionality. In addition, higher correlation among contiguous bands increases the redundancy within the bands. Hence, dimension reduction of hyperspectral data is very crucial so as to obtain good classification accuracy results. This paper presents a new feature selection technique. Non-negative Matrix Factorization (NMF) algorithm is proposed to obtain reduced relevant features in the input domain of each class label. This aimed to reduce classification error and dimensionality of classification challenges. Indiana pines of the Northwest Indiana dataset is used to evaluate the performance of the proposed method through experiments of features selection and classification. The Waikato Environment for Knowledge Analysis (WEKA) data mining framework is selected as a tool to implement the classification using Support Vector Machines and Neural Network. The selected features subsets are subjected to land cover classification to investigate the performance of the classifiers and how the features size affects classification accuracy. Results obtained shows that performances of the classifiers are significant. The study makes a positive contribution to the problems of hyperspectral imagery by exploring NMF, SVMs and NN to improve classification accuracy. The performances of the classifiers are valuable for decision maker to consider tradeoffs in method accuracy versus method complexity.
Abe, Bolanle T.; Jordaan, J. A.
Hyperspectral imaging is a potential noninvasive technology for detecting, separating and identifying various substances. In the forensic and military medicine and other CBRNE related use it could be a potential method for analyzing blood and for scanning other human based fluids. For example, it would be valuable to easily detect whether some traces of blood are from one or more persons or if there are some irrelevant substances or anomalies in the blood. This article represents an experiment of separating four persons' blood stains on a white cotton fabric with a SWIR hyperspectral camera and FT-NIR spectrometer. Each tested sample includes standardized 75 _l of 100 % blood. The results suggest that on the basis of the amount of erythrocytes in the blood, different people's blood might be separable by hyperspectral analysis. And, referring to the indication given by erythrocytes, there might be a possibility to find some other traces in the blood as well. However, these assumptions need to be verified with wider tests, as the number of samples in the study was small. According to the study there also seems to be several biological, chemical and physical factors which affect alone and together on the hyperspectral analyzing results of blood on fabric textures, and these factors need to be considered before making any further conclusions on the analysis of blood on various materials.
Kuula, J.; Puupponen, H.-H.; Rinta, H.; Pölönen, I.
we are developing is to address two important challenges in biomedical imaging: 3-D in vivo nearAbstract-- We are developing a hyperspectral imaging system aimed at imaging fluorescent molecules in two different contexts: In vivo 3-D molecular imaging of mice and multi-fluorophore imaging of 2-D
Leahy, Richard M.
This article presents a novel method for the enhancement of the spatial quality of hyperspectral (HS) images through the use of a high resolution panchromatic (PAN) image. Due to the high number of bands, the application of a pan-sharpening technique to HS images may result in an increase of the computational load and complexity. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. To solve this problem, we propose a pan-sharpening technique combining both dimensionality reduction and fusion, making use of non-linear principal component analysis (NLPCA) and Indusion, respectively, to enhance the spatial resolution of a HS image. We have tested the proposed algorithm on HS images obtained from CHRIS-Proba sensor and PAN image obtained from World view 2 and demonstrated that a reduction using NLPCA does not result in any significant degradation in the pan-sharpening results.
Licciardi, Giorgio Antonino; Khan, Muhammad Murtaza; Chanussot, Jocelyn; Montanvert, Annick; Condat, Laurent; Jutten, Christian
VTT Technical Research Centre of Finland has developed Tunable Fabry-Perot Interferometer (FPI) based miniaturized hyperspectral imager which can be operated from light weight Unmanned Aerial Vehicles (UAV). The concept of the hyperspectral imager has been published in the SPIE Proc. 7474, 8174 and 8374. This instrument requires dedicated laboratory and on-board calibration procedures which are described. During summer 2012 extensive UAV Hyperspectral imaging campaigns in the wavelength range 400 - 900 nm at resolution range 10 - 40 nm @ FWHM were performed to study forest inventory, crop biomass and nitrogen distributions and environmental status of natural water applications. The instrument includes spectral band limiting filters which can be used for the on-board wavelength scale calibration by scanning the FPI pass band center wavelength through the low and high edge of the operational wavelength band. The procedure and results of the calibration tests will be presented. A short summary of the performed extensive UAV imaging campaign during summer 2012 will be presented.
Saari, Heikki; Pölönen, Ilkka; Salo, Heikki; Honkavaara, Eija; Hakala, Teemu; Holmlund, Christer; Mäkynen, Jussi; Mannila, Rami; Antila, Tapani; Akujärvi, Altti
A spectroscopic camera has been developed which has spectral resolution of less than 1.5nm in the ultraviolet (UV) and visible wavelength bands (320-580 nm). Its main components are a specially coated UV objective lens, a UV Acousto-Optic Tunable Filter (AOTF) with a thermo-electric cooling system, and a imaging system based on a high-gain avalanche rushing amorphous photoconductor (HARP) developed by NHK Science and Technical Research Laboratories. Research is currently under way to develop the hyperspectral camera into a sensor package for airborne and ultimately space-based remote sensing applications. This paper presents the basic principle and configuration of the hyperspectral camera, and gives details of tests to measure its performance. The results of spectral resolution tests analyzing very close two spectra from a helium-discharge lamp demonstrate the camera's high spectral resolution performance. Full color and spectral images obtained by a spectrometry experiment are also presented to demonstrate the camera's hyperspectral capabilities.
Kurosaki, Hirohisa; Shingu, Hirokimi; Enkyo, Shigeharu; Suzuki, Takao; Tanioka, Kenkichi; Takefuji, Yoshiyasu
In this article, we report a hyperspectral optical imaging application for measurement of the reflectance spectra of photonic structures that produce structural colors with high spatial resolution. The measurement of the spectral reflectance function is exemplified in the butterfly wings of two different species of Lepidoptera: the blue iridescence reflected by the nymphalid Morpho didius and the green iridescence of the papilionid Papilio palinurus. Color coordinates from reflectance spectra were calculated taking into account human spectral sensitivity. For each butterfly wing, the observed color is described by a characteristic color map in the chromaticity diagram and spreads over a limited volume in the color space. The results suggest that variability in the reflectance spectra is correlated with different random arrangements in the spatial distribution of the scales that cover the wing membranes. Hyperspectral optical imaging opens new ways for the non-invasive study and classification of different forms of irregularity in structural colors.
Medina, José Manuel; Nascimento, Sérgio Miguel Cardoso; Vukusic, Pete
Freshness of pork is an important quality attribute, which can vary greatly in storage and logistics. The specific objectives of this research were to develop a hyperspectral imaging system to predict pork freshness based on quality attributes such as total volatile basic-nitrogen (TVB-N), pH value and color parameters (L*,a*,b*). Pork samples were packed in seal plastic bags and then stored at 4°C. Every 12 hours. Hyperspectral scattering images were collected from the pork surface at the range of 400 nm to 1100 nm. Two different methods were performed to extract scattering feature spectra from the hyperspectral scattering images. First, the spectral scattering profiles at individual wavelengths were fitted accurately by a three-parameter Lorentzian distribution (LD) function; second, reflectance spectra were extracted from the scattering images. Partial Least Square Regression (PLSR) method was used to establish prediction models to predict pork freshness. The results showed that the PLSR models based on reflectance spectra was better than combinations of LD "parameter spectra" in prediction of TVB-N with a correlation coefficient (r) = 0.90, a standard error of prediction (SEP) = 7.80 mg/100g. Moreover, a prediction model for pork freshness was established by using a combination of TVB-N, pH and color parameters. It could give a good prediction results with r = 0.91 for pork freshness. The research demonstrated that hyperspectral scattering technique is a valid tool for real-time and nondestructive detection of pork freshness.
Li, Yongyu; Zhang, Leilei; Peng, Yankun; Tang, Xiuying; Chao, Kuanglin; Dhakal, Sagar
A three-dimensional (3D) wavelet coder based on 3D significance tree splitting is proposed for hyperspectral image compression. 3D discrete wavelet transform (DWT) is applied to explore the spatial and spectral correlations. Then the 3D significance tree structure is constructed in 3D wavelet domain, and wavelet coefficients are encoded via 3D significance tree splitting. This proposed algorithm does not need to
Jing Huang; Rihong Zhu; Jianxin Li; Yong He
Hyperspectral imaging of the Earth's surface in the visible and near infrared from the CLARREO mission requires high levels of radiometric accuracy with stability maintained on-orbit. Traditional approaches rely on ground- based calibrations for accuracy and on-board sources for tracking post-launch changes in instrument sensitivity. The proposed on-orbit cross-calibration approach improves radiometric accuracy and stability by transferring the highly accurate
G. Kopp; P. Pilewskie; D. Harber; J. Harder; B. McClintock; R. Lewis; E. Richard; T. Sparn; J. Young
Introduction: In preparation for the upcoming International Mars Sample Return mission (MSR), returning samples containing potential biohazards, we have implemented a hyperspec-tral method of in-situ analysis of grains performed in BSL4 quarantine conditions, by combining several non-destructive imaging diagnostics. This allows sample transportation on optimized experimental setups, while monitoring the sample quarantine conditions. Our hyperspectral methodology was tested during analyses of meteorites [1-2] and cometary and interstellar grains from the recent NASA Stardust mission [3-6]. Synchrotron Radiation protocols: X-ray analysis methods are widely accepted as the least destructive probes of fragile, unique samples. Diffraction, X-ray fluorescence and ab-sorption micro/nano-spectroscopies were performed on chondritic test samples using focused monochromatic beams at the ESRF synchrotron in Grenoble, France. 2D maps of grain com-position down to ppm concentrations and polycrystalline structure have simultaneously been acquired, followed by X-ray absorption performed on elements of Z 26. Ideally, absorption micro-tomography can later be performed in full-beam mode to record the 3D morphology of the grain followed by fluorescence-tomography in focus-beam mode which complements this picture with a 3D elemental image of the grain. Lab-based protocols: Raman and IR-based spectroscopies have been performed in reflection mode for mineralogical imaging of the grains in the laboratory using commercial microscopes. The spatial resolution varied in the 1-10 m range. Laser limited penetration of opaque samples permits only 2D imaging of the few nanometer-thick outer layers of the grains. Mineralogical maps are now routinely acquired using Raman spectroscopy at sub-micron scales through the 3 container walls of the Martian sample holder, followed by IR few-micrometer spot measurements recording C-based and potential aqueous alteration distributions. Sample Holder: A miniaturized sample-holder  has been designed and built to allow direct analyses of a set of extraterrestrial grains confined in a sealed triple container and remotely po-sitioned in front of the X-ray or laser beams of the various setups. The grains are held in several thin walls (10 m) ultrapure silica capillaries which are sufficiently resistant for manual/remote-controlled micro-manipulation but semitransparent for the characteristic X-rays, Raman and IR radiations. Miniaturized pressure/temperature sensors located in each container periodically monitor the integrity of the ensemble, ensuring BSL4 condi-tions. References:  B. Golosio, A. Simionovici, A. Somogyi, L. Lemelle, M. Chukalina, A. Brunetti, Jrnl. of App. Phys. 94, 145-157, 2003  L. Lemelle, A. Simionovici, R. Truche, Ch. Rau, M. Chukalina, Ph. Gillet, Am. Min. 87 , 547-553, 2004  Michael E. Zolensky et al., Science 314, 1735-1739, 2006  G. J. Flynn et al., Science 314, 1731-1735, 2006  Pierre Bleuet, Alexandre Simionovici, Laurence Lemelle, Tristan Ferroir, Peter Cloetens, Rémi Tu-coulou, Jean Susini, App. Phys. Lett. 92, 213111-1-3, 2008.  A. J. Westphal, et al., AIP Proceedings of the ICXOM Congress, (in print) 2010.  A. Simionovici and CNES, patent pending.
Simionovici, Alexandre; Viso, Michel; Beck, Pierre; Lemelle, Laurence; Westphal, Andrew; Vincze, Laszlo; Schoonjans, Tom; Fihman, Francois; Chazalnoel, Pascale; Ferroir, Tristan; Solé, Vicente Armando; Tucoulou, R.
background. Application areas include hyperspectral NIR images for food quality control, analysis of simple multispectral data in terms of image quality with implications for noise removal," IEEE TransactionsKernel Methods in Orthogonalization of Near-Infrared Hyperspectral Images of Maize Kernels Allan
An Evaluation Framework and a Benchmark For Multi/Hyperspectral Image Compression Jonathan Delcourt investigate different approaches for multi/hyperspectral image compression. In particular, we compare compression algorithms need to be adapted to this type of image. One of the most efficient compression methods
A new hyperspectral image compression algorithm based on bit plane transform is proposed. The main idea of the bit plane transform is to decompose the hyperspectral image into a series of bi-level images which can be compressed more efficient. The gray code and band sequence optimization techniques are adopted to improve the compression performance in the preprocess stage. The main
HengShu Liu; Liping Zhang; LianQing Huang
In using partial transform for the coding of hyperspectral image, it must consider spectral decorrelation between image components, issues that a combined adaptive classification and partial transform algorithm for hyperspectral image compression is presented in this paper. Our method uses a linear prediction based on adaptive classification to decorrelate the spectrum redundancy and a 2D integer reversible DCT-based scheme as
Zheng Zhou; Jian Liu; Jinwen Tian
The HICO (hyperspectral imager for the coastal ocean) program is a collaboration between the Naval Research Laboratory (NRL), the University of Hawai'i at Manoa, Utah State University, and NovaSol Inc., to image the coastal ocean and reef systems from the International Space Station. The first phase of the program will install the NRL portable hyperspectral imager for low light spectroscopy
M. R. Corson; J. H. Bowles; W. Chen; C. O. Davis; K. H. Gallelli; D. R. Korwan; P. G. Lucey; T. J. Mosher; R. Holasek
In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.
Taher, Akar; Chehdi, Kacem; Cariou, Claude
Nondestructive sensing is critical to assuring postharvest quality of apple fruit and consumer acceptance and satisfaction.\\u000a The objective of this research was to use a hyperspectral scattering technique to acquire spectral scattering images from\\u000a apple fruit and develop a data analysis method relating hyperspectral scattering characteristics to fruit firmness and soluble\\u000a solids content (SSC). Hyperspectral scattering images were obtained from
This paper presents a low-cost hyperspectral measurement setup in a new application based on fluorescence detection in the visible (Vis) wavelength range. The aim of the setup is to take hyperspectral fluorescence images of viscous materials. Based on these images, fluorescent and non-fluorescent impurities in the viscous materials can be detected. For the illumination of the measurement object, a narrow-band high-power light-emitting diode (LED) with a center wavelength of 370 nm was used. The low-cost acquisition unit for the imaging consists of a linear variable filter (LVF) and a complementary metal oxide semiconductor (CMOS) 2D sensor array. The translucent wavelength range of the LVF is from 400 nm to 700 nm. For the confirmation of the concept, static measurements of fluorescent viscous materials with a non-fluorescent impurity have been performed and analyzed. With the presented setup, measurement surfaces in the micrometer range can be provided. The measureable minimum particle size of the impurities is in the nanometer range. The recording rate for the measurements depends on the exposure time of the used CMOS 2D sensor array and has been found to be in the microsecond range. PMID:24064604
Murr, Patrik J.; Schardt, Michael; Koch, Alexander W.
This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem, and is solved with the Alternating Direction Method of Multipliers. The second one accounts for signal-dependent noise, and is addressed with a Reweighted Least Squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach.
Ammanouil, Rita; Ferrari, Andre; Richard, Cedric; Mary, David
Hyperspectral microscope imaging (HMI) method which provides both spatial and spectral information can be effective for foodborne pathogen detection. The AOTF-based hyperspectral microscope imaging method can be used to characterize spectral properties of biofilm formed by Salmonella enteritidis as well as Escherichia coli. The intensity of spectral imagery and the pattern of spectral distribution varied with system parameters (integration time and gain) of HMI system. The preliminary results demonstrated determination of optimum parameter values of HMI system and the integration time must be no more than 250 ms for quality image acquisition from biofilm formed by S. enteritidis. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 498, 522, 550 and 594 nm were distinctive for biofilm; whereas, the intensity of spectral images at 546 nm was distinctive for E. coli. For more accurate comparison of intensity from spectral images, a calibration protocol, using neutral density filters and multiple exposures, need to be developed to standardize image acquisition. For the identification or classification of unknown food pathogen samples, ground truth regions-of-interest pixels need to be selected for "spectrally pure fingerprints" for the Salmonella and E. coli species.
Park, Bosoon; Lee, Sangdae; Yoon, Seung-Chul; Sundaram, Jaya; Windham, William R.; Hinton, Arthur, Jr.; Lawrence, Kurt C.
The spectroscopy of analyte-specific molecular vibrations in tissue thin sections has opened up a path toward histopathology without the need for tissue staining. However, biomedical vibrational imaging has not yet advanced from academic research to routine histopathology due to long acquisition times for the microscopic hyperspectral images and/or cost and availability of the necessary equipment. Here we show that the combination of a fast-tuning quantum cascade laser with a microbolometer array detector allows for a rapid image acquisition and bares the potential for substantial cost reduction. A 3.1 x 2.8 mm2 unstained thin section of mouse jejunum has been imaged in the 9.2 to 9.7 ?m wavelength range (spectral resolution ~1 cm(-1)) within 5 min with diffraction limited spatial resolution. The comparison of this hyperspectral imaging approach with standard Fourier transform infrared imaging or mapping of the identical sample shows a reduction in acquisition time per wavenumber interval and image area by more than one or three orders of magnitude, respectively. PMID:24967840
Kröger, Niels; Egl, Alexander; Engel, Maria; Gretz, Norbert; Haase, Katharina; Herpich, Iris; Kränzlin, Bettina; Neudecker, Sabine; Pucci, Annemarie; Schönhals, Arthur; Vogt, Jochen; Petrich, Wolfgang
The spectroscopy of analyte-specific molecular vibrations in tissue thin sections has opened up a path toward histopathology without the need for tissue staining. However, biomedical vibrational imaging has not yet advanced from academic research to routine histopathology due to long acquisition times for the microscopic hyperspectral images and/or cost and availability of the necessary equipment. Here we show that the combination of a fast-tuning quantum cascade laser with a microbolometer array detector allows for a rapid image acquisition and bares the potential for substantial cost reduction. A 3.1×2.8 mm2 unstained thin section of mouse jejunum has been imaged in the 9.2 to 9.7 ?m wavelength range (spectral resolution ˜1 cm-1) within 5 min with diffraction limited spatial resolution. The comparison of this hyperspectral imaging approach with standard Fourier transform infrared imaging or mapping of the identical sample shows a reduction in acquisition time per wavenumber interval and image area by more than one or three orders of magnitude, respectively.
Kröger, Niels; Egl, Alexander; Engel, Maria; Gretz, Norbert; Haase, Katharina; Herpich, Iris; Kränzlin, Bettina; Neudecker, Sabine; Pucci, Annemarie; Schönhals, Arthur; Vogt, Jochen; Petrich, Wolfgang
In order to conduct rational management of watering lettuce, the model of detecting lettuce leaves' moisture was built. First of all, the hyperspectral images of lettuce leaves were acquired and simultaneously the moisture proportions of leaves were measured. Meanwhile, hyperspectral images were analyzed and the characteristic bands of lettuce leaves' moisture were found. Then the images in characteristic bands were processed and the image features of lettuce leaves' moisture were computed. The image features highly relevant to moisture were obtained through correlation analysis. Furthermore, due to the possible correlation among image features, the principal components of the images were extracted by principal components analysis and were used as BP neural network's inputs to establish PCA-ANN model. At the same time, other models were constructed by using BP neural network and traditional MLR (multiple liner regression) method respectively. Prediction examinations of the three models were made based on the same sample data. The experimental results show that the average prediction error of PCA-ANN prediction model of tillering stage reaches 9.323% which is improved compared with BP-ANN and MLR prediction models. PMID:23697146
Sun, Jun; Wu, Xiao-Hong; Zhang, Xiao-Dong; Gao, Hong-Yan
We describe experimental results on the detection of explosives residues using active hyperspectral imaging by illumination of the target surface using an external cavity quantum cascade laser (ECQCL) and imaging using a room temperature microbolometer camera. The active hyperspectral imaging technique forms an image hypercube by recording one image for each tuning step of the ECQCL. The resulting hyperspectral image contains the full absorption spectrum produced by the illumination laser at each pixel in the image which can then be used to identify the explosive type and relative quantity using spectral identification approaches developed initially in the remote sensing community.
Bernacki, Bruce E.; Phillips, Mark C.
Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process and under- or overestimation of this number may lead to incorrect unmixing in unsupervised methods. In this paper, we discuss a new method for determining the intrinsic dimension using recent advances in random matrix theory. This method is entirely unsupervised, free from any user-determined parameters and allows spectrally correlated noise in the data. Robustness tests are run on synthetic data, to determine how the results were affected by noise levels, noise variability, noise approximation, and spectral characteristics of the endmembers. Success rates are determined for many different synthetic images, and the method is tested on two pairs of real images, namely a Cuprite scene taken from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) and SpecTIR sensors, and a Lunar Lakes scene taken from AVIRIS and Hyperion, with good results. PMID:23193450
Cawse-Nicholson, Kerry; Damelin, Steven B; Robin, Amandine; Sears, Michael
computing Adaptive run-time data compression Wavelet transform Hyperspectral imaging Remote sensing a b of parallel hyperspectral imaging algorithms is directly related to the amount of information to be exchangedImproving the scalability of hyperspectral imaging applications on heterogeneous platforms using
Plaza, Antonio J.
We present a study on the the applicability of hyperspectral images to evaluate color filter array (CFA) design and the performance of demosaicking algorithms. The aim is to simulate a typical digital still camera processing pipe-line and to compare two different scenarios: evaluate the performance of demosaicking algorithms applied to raw camera RGB values before color rendering to sRGB, and evaluate the performance of demosaicking algorithms applied on the final sRGB color rendered image. The second scenario is the most frequently used one in literature because CFA design and algorithms are usually tested on a set of existing images that are already rendered, such as the Kodak Photo CD set containing the well-known lighthouse image. We simulate the camera processing pipe-line with measured spectral sensitivity functions of a real camera. Modeling a Bayer CFA, we select three linear demosaicking techniques in order to perform the tests. The evaluation is done using CMSE, CPSNR, s-CIELAB and MSSIM metrics to compare demosaicking results. We find that the performance, and especially the difference between demosaicking algorithms, is indeed significant depending if the mosaicking/demosaicking is applied to camera raw values as opposed to already rendered sRGB images. We argue that evaluating the former gives a better indication how a CFA/demosaicking combination will work in practice, and that it is in the interest of the community to create a hyperspectral image dataset dedicated to that effect.
Larabi, Mohamed-Chaker; Süsstrunk, Sabine
Superpixels are homogeneous image regions comprised of several contiguous pixels. They are produced by shattering the image into contiguous, homogeneous regions that each cover between 20 and 100 image pixels. The segmentation aims for a many-to-one mapping from superpixels to image features; each image feature could contain several superpixels, but each superpixel occupies no more than one image feature. This conservative segmentation is relatively easy to automate in a robust fashion. Superpixel processing is related to the more general idea of improving hyperspectral analysis through spatial constraints, which can recognize subtle features at or below the level of noise by exploiting the fact that their spectral signatures are found in neighboring pixels. Recent work has explored spatial constraints for endmember extraction, showing significant advantages over techniques that ignore pixels relative positions. Methods such as AMEE (automated morphological endmember extraction) express spatial influence using fixed isometric relationships a local square window or Euclidean distance in pixel coordinates. In other words, two pixels covariances are based on their spatial proximity, but are independent of their absolute location in the scene. These isometric spatial constraints are most appropriate when spectral variation is smooth and constant over the image. Superpixels are simple to implement, efficient to compute, and are empirically effective. They can be used as a preprocessing step with any desired endmember extraction technique. Superpixels also have a solid theoretical basis in the hyperspectral linear mixing model, making them a principled approach for improving endmember extraction. Unlike existing approaches, superpixels can accommodate non-isometric covariance between image pixels (characteristic of discrete image features separated by step discontinuities). These kinds of image features are common in natural scenes. Analysts can substitute superpixels for image pixels during endmember analysis that leverages the spatial contiguity of scene features to enhance subtle spectral features. Superpixels define populations of image pixels that are independent samples from each image feature, permitting robust estimation of spectral properties, and reducing measurement noise in proportion to the area of the superpixel. This permits improved endmember extraction, and enables automated search for novel and constituent minerals in very noisy, hyperspatial images. This innovation begins with a graph-based segmentation based on the work of Felzenszwalb et al., but then expands their approach to the hyperspectral image domain with a Euclidean distance metric. Then, the mean spectrum of each segment is computed, and the resulting data cloud is used as input into sequential maximum angle convex cone (SMACC) endmember extraction.
Thompson, David R.; Castano, Rebecca; Gilmore, Martha
-going and planned remote sensing missions. 1. Introduction Hyperspectral imagers such as the NASA Jet Propulsion Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results indicate that heterogeneous of algorithm analysis . Despite the growing interest in hyperspectral imaging research, only a few parallel
Plaza, Antonio J.
Near-infrared (NIR) hyperspectral imaging technique was investigated for the detection of bruises on pickling cucumbers caused by mechanical stress. An NIR hyperspectral imaging system was developed to acquire both spatial and spectral information from pickling cucumbers in the spectral region of 9...
exploitation. In most cases, real-time or nearly real-time processing of hyperspectral images is required- rithm in hyperspectral image interpretation is the automatic target generation process (ATGPCommodity Cluster-Based Parallel Implementation of an Automatic Target Generation Process
Plaza, Antonio J.
HICO and RAIDS Experiment Payload - Hyperspectral Imager For The Coastal Ocean (HREP-HICO) will operate a visible and near-infrared (VNIR) Maritime Hyperspectral Imaging (MHSI) system, to detect, identify and quantify coastal geophysical features from the International Space Station.
Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a
Yao-Ze Feng; Da-Wen Sun
The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system
Gamal Elmasry; Mohammed Kamruzzaman; Da-Wen Sun; Paul Allen
An acousto-optic tunable filter-based hyperspectral microscope imaging method has potential for identification of foodborne pathogenic bacteria from microcolony rapidly with a single cell level. We have successfully developed the method to acquire quality hyperspectral microscopic images from variou...
We present an algorithm for lossy compression of hyperspectral images for imple- mentation on fieldReduced Complexity Wavelet-Based Predictive Coding of Hyperspectral Images for FPGA Implementation is compressed using the Set Partitioning in Hierarchical Trees algorithm. To reduce the complexity
Active hyperspectral imaging using a quantum cascade laser (QCL) array and digital-pixel focal hyperspectral imaging using a quantum- cascade laser (QCL) array as the illumination source and a digital of explosives using widely tunable mid-infrared quantum cascade lasers," Opt. Eng. 49(11), 111127 (2010). 4. M
1 Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model Toulouse cedex 7, France Abstract This paper presents a nonlinear mixing model for hyperspectral image functions are approximated using polynomial functions leading to a polynomial post-nonlinear mixing model
segmentation for analysis of hyperspectral data sets, with application to Compact Reconnaissance ImagingSuperpixel segmentation for analysis of hyperspectral data sets, with application to Compact Reconnaissance Imaging Spectrometer for Mars data, Moon Mineralogy Mapper data, and Ariadnes Chaos, Mars Martha S
This paper presents a new method for detecting poultry skin tumors in hyperspectral reflectance images. We employ the principal component analysis (PCA), discrete wavelet transform (DWT), and kernel discriminant analysis (KDA) to extract the independent feature sets in hyperspectral reflectance imag...
The mining industry is plagued with socioeconomic and safety roadblocks with not many solutions in the midst of a demanding market. As more and more geologic research using hyperspectral technology has been performed, along with an affordable price point for commercial use of hyperspectral technology, the benefits of hyperspectral imaging to the mining industry has become apparent. This study identifies the key areas of use for hyperspectral imaging in the mining industry through a case study of gypsum mine samples obtained from a mine in central Tuscany.
Wilson, Janette H.; Greenberger, Rebecca N.
Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.
Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.
A novel dual-band hyperspectral imaging system has been used to collect field test data for robotics vision applications. The imaging system can collect full scene hyperspectral images in both the long wave infrared (LWIR) band (8-10.5 ?m) and the mid-wave infrare (MWIR) band (4-5.25 ?m) simultaneously. The imager uses a specially designed Ge diffractive lens with a dual-band 320×240 HgCdTe infrared focal plane array (FPA) cooled with a Sterling cooler. The stacked FPA consists of two layers: the top one sensitive in the MWIR region and the bottom one sensitive in the LWIR region. The diffractive lens is designed to focus a first order, single-color (i.e., 8.0 ?m) image in the LWIR onto the bottom layer of the FPA while at the same time focusing a second order single-color (i.e., 4.0 ?m) image in the MWIR onto the top layer of the FPA. Images at different wavelengths are acquired by moving the lens along its optical axis. Moving the lens over the entire range during data collection allows sequential collection of spectral images in each band resulting in the collection of two complete image cubes. The focal length of the lens is 75 mm at 9 ?m. The spectral resolution of the imager is 0.1 ?m at the 9 ?m wavelength. In general, 128 narrow wavelength bands are collected in each of the two broad spectral regions. After data collection, the images are processed to remove noise, contributions from unfocused wavelengths, and magnification differences. A description of the imager, data collection, noise removal, post-processing, and analysis are presented.
Smith, Dale J.; Gupta, Neelam
Two AIRIS sensors were tested at Dugway Proving Grounds against chemical agent vapor simulants. The primary objectives of the test were to: 1) assess performance of algorithm improvements designed to reduce false alarm rates with a special emphasis on solar effects, and 3) evaluate performance in target detection at 5 km. The tests included 66 total releases comprising alternating 120 kg glacial acetic acid (GAA) and 60 kg triethyl phosphate (TEP) events. The AIRIS sensors had common algorithms, detection thresholds, and sensor parameters. The sensors used the target set defined for the Joint Service Lightweight Chemical Agent Detector (JSLSCAD) with TEP substituted for GA and GAA substituted for VX. They were exercised at two sites located at either 3 km or 5 km from the release point. Data from the tests will be presented showing that: 1) excellent detection capability was obtained at both ranges with significantly shorter alarm times at 5 km, 2) inter-sensor comparison revealed very comparable performance, 3) false alarm rates < 1 incident per 10 hours running time over 143 hours of sensor operations were achieved, 4) algorithm improvements eliminated both solar and cloud false alarms. The algorithms enabling the improved false alarm rejection will be discussed. The sensor technology has recently been extended to address the problem of detection of liquid and solid chemical agents and toxic industrial chemical on surfaces. The phenomenology and applicability of passive infrared hyperspectral imaging to this problem will be discussed and demonstrated.
Marinelli, William J.; Miyashiro, Rex; Gittins, Christopher M.; Konno, Daisei; Chang, Shing; Farr, Matt; Perkins, Brad
There exist microscopes that are able to obtain the chemical properties of a sample, because there are some cases in which it is difficult to find out causality of a phenomenon by using only the structural information of a sample. Obtaining the chemical properties of a sample is important in biomedical imaging, because most biological phenomena include changes in the chemical properties of the sample. Hyperspectral imaging (HSI) is one of the popular imaging methods for characterizing materials and biological samples by measuring the reflectance or emission spectrum of the sample. Because all materials have a unique reflectance spectrum, it is possible to analyze material properties and detect changes in the chemical properties of a sample by measuring the spectral changes with respect to the original spectrum. Because of its ability to measure the spectrum of a sample, HSI is widely used in materials identification applications such as aerial reconnaissance and is the subject of various studies in microscopy. Although there are many advantages to using the method, conventional HSI has some limitations because of its complex configuration and slow speed. In this research we propose a new type of multimodal confocal hyperspectral imaging microscopy with fast image acquisition and a simple configuration that is capable of both confocal and HSI microscopies.
Kim, Young-Duk; Do, Dukho; Yoo, Hongki; Gweon, DaeGab
A considerable amount research is being conducted on microalgae, since microalgae are becoming a promising source of renewable energy. Most of this research is centered on lipid production in microalgae because microalgae produce triacylglycerol which is ideal for biodiesel fuels. Although we are interested in research to increase lipid production in algae, we are also interested in research to sustain healthy algal cultures in large scale biomass production farms or facilities. The early detection of fluctuations in algal health, productivity, and invasive predators must be developed to ensure that algae are an efficient and cost-effective source of biofuel. Therefore we are developing technologies to monitor the health of algae using spectroscopic measurements in the field. To do this, we have proposed to spectroscopically monitor large algal cultivations using LIDAR (Light Detection And Ranging) remote sensing technology. Before we can deploy this type of technology, we must first characterize the spectral bio-signatures that are related to algal health. Recently, we have adapted our confocal hyperspectral imaging microscope at Sandia to have two-photon excitation capabilities using a chameleon tunable laser. We are using this microscope to understand the spectroscopic signatures necessary to characterize microalgae at the cellular level prior to using these signatures to classify the health of bulk samples, with the eventual goal of using of LIDAR to monitor large scale ponds and raceways. By imaging algal cultures using a tunable laser to excite at several different wavelengths we will be able to select the optimal excitation/emission wavelengths needed to characterize algal cultures. To analyze the hyperspectral images generated from this two-photon microscope, we are using Multivariate Curve Resolution (MCR) algorithms to extract the spectral signatures and their associated relative intensities from the data. For this presentation, I will show our two-photon hyperspectral imaging results on a variety of microalgae species and show how these results can be used to characterize algal ponds and raceways.
Sinclair, Michael B.; Melgaard, David Kennett; Reichardt, Thomas A. (Sandia National Laboratories, Livermore, CA); Timlin, Jerilyn Ann; Garcia, Omar Fidel; Luk, Ting Shan; Jones, Howland D. T.; Collins, Aaron M.
In this paper, an algorithm for hyperspectral image compression is presented. It carries DCT (Discrete Cosine Transform) on spectral bands to exploit the spectral correlation and then DWT (Discrete Wavelet Transform) on every eigen image to exploit the spatial correlation. After that, 3D-SPIHT (three-dimensional Set Partitioning in Hierarchical Trees) is performed for encoding. Experiments were done on the OMIS-I (Operational Modular Imaging Spectrometer) image and the performance of this algorithm was compared with that of 2D-SPIHT. The results show that the performance of 3D-SPIHT based on DCT and DWT is much better than that of 2D-SPIHT and the quality of the reconstructed images is satisfying.
Wei, Haiping; Zhao, Baojun; He, Peikun
Lightning and Atmospheric Electricity Research at the Global Hydrology and Climate Center provides these "GIF images showing a graphical representation of the Lightning Imaging Sensor orbit data for each day." The lightning distribution images are available by clicking on highlighted spots on a global map. Data are released one month at a time and currently include December 1997 to the present.
We present a new hyperspectral reflected light microscopy system with a scanned broadband supercontinuum light source. This wide-field and low phototoxic hyperspectral imaging system has been successful for performing spectral three-dimensional (3D) localization and spectroscopic identification of CD44-targeted PEGylated AuNPs in fixed cell preparations. Such spatial and spectral information is essential for the improvement of nanoplasmonic-based imaging, disease detection and treatment in complex biological environment. The presented system can be used for real-time 3D NP tracking as spectral sensors, thus providing new avenues in the spatio-temporal characterization and detection of bioanalytes. 3D image of the distribution of functionalized AuNPs attached to CD44-expressing MDA-MB-231 human cancer cells. PMID:24961507
Tropical mangrove forests along the coast evolve dynamically due to constant changes in the natural ecosystem and ecological cycle. Remote sensing has paved the way for periodic monitoring and conservation of such floristic resources, compared to labour intensive in-situ observations. With the laboratory quality image spectra obtained from hyperspectral image data, species level discrimination in habitats and ecosystems is attainable. One of the essential steps before classification of hyperspectral image data is band selection. It is important to eliminate the redundant bands to mitigate the problems of Hughes effect that are likely to affect further image analysis and classification accuracy. This paper presents a methodology for the selection of appropriate hyperspectral bands from the EO-1 Hyperion image for the identification and mapping of mangrove species and coastal landcover types in the Bhitarkanika coastal forest region, eastern India. Band selection procedure follows class based elimination procedure and the separability of the classes are tested in the band selection process. Individual bands are de-correlated and redundant bands are removed from the bandwise correlation matrix. The percent contribution of class variance in each band is analysed from the factors of PCA component ranking. Spectral bands are selected from the wavelength groups and statistically tested. Further, the band selection procedure is compared with similar techniques (Band Index and Mutual information) for validation. The number of bands in the Hyperion image was reduced from 196 to 88 by the Factor-based ranking approach. Classification was performed by Support Vector Machine approach. It is observed that the proposed Factor-based ranking approach performed well in discriminating the mangrove species and other landcover units compared to the other statistical approaches. The predominant mangrove species Heritiera fomes, Excoecaria agallocha and Cynometra ramiflora are spectral identified and the health status of these species are assessed by the selected band. Further, the performance of this band selection approaches are evaluated in multi-sensor image fusion for better mapping of mangrove ecosystems, wherein spatial resolution is enhanced while retaining the optimal number of hyperspectral bands.
Ashokkumar, L.; Shanmugam, S.
In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.
Wang, Li-Guo; Liu, Dan-Feng; Zhao, Liang
A spatial method of multistructure sampling based rotation-invariant uniform local binary pattern (named MsLBPriu2) for classification of hyperspectral images is proposed. This method exploits the local property (micro-/macrostructure) of local image patches encoded in the classifier by considering a local neighboring structure around each central pixel and can well suppress the difference of rotational textures for each multicluster class. The proposed method is simple yet efficient for extracting isotropic and anisotropic spatial features from local image patches via different extended sampling on circular regions and elliptical ones with four different rotational angles. Furthermore, the rotation-invariant characteristic of extracted isotropic features is achieved by the inclusion of a rotation-invariant uniform LBP operator. Moreover, the proposed method becomes more robust with respect to the within-class variation. Finally, different classifiers, support vector machine, K-nearest neighbor, and linear discriminant analysis, are compared to evaluate MsLBPriu2 and other feature sets/entropy-based query-by-bagging active learning. We demonstrate the performance of our approach on four different hyperspectral remote sensing images. Experimental results show that the new set of reduced spatial features has a better performance than a variety of state-of-the-art classification algorithms.
Bian, Xiaoyong; Zhang, Xiaolong; Liu, Renfeng; Ma, Li; Fu, Xiaowei
In this paper, we propose two compression methods for hyperspectral images with discriminant features enhanced. Generally, when hyperspectral images are compressed with conventional image compression algorithms, which mainly minimize mean squared errors, discriminant features of the original data may not be well preserved since they may not be necessarily large in energy. In this paper, we propose two compression methods that do preserve the discriminant information. In the first method, we enhanced the discriminant features and then compressed the enhanced data using conventional image compression algorithms such as 3D JPEG 2000. In the second method, we applied a feature extraction method and extracted the discriminantly dominant feature vectors. By examining the dominant feature vectors, we determined the discriminant usefulness of each spectral band. Based on these findings, we determined the bit allocation of each spectral band assuming 2D compression methods are used. Experiments show that the proposed methods effectively preserved the discriminant information and yielded improved classification accuracies compared to existing compression algorithms.
Lee, Chulhee; Choi, Euisun; Jeong, Taeuk; Lee, Sangwook; Lee, Jonghwa
There is an increasing demand for wavelength agile laser sources covering the mid-infrared (MIR, 3.5-12 µm) wavelength range, among others in active imaging. The MIR range comprises a particularly interesting part of the electromagnetic spectrum for active hyperspectral imaging applications, due to the fact that the characteristic `fingerprint' absorption spectra of many chemical compounds lie in that range. Conventional semiconductor diode laser technology runs out of steam at such long wavelengths. For many applications, MIR coherent light sources based on solid state lasers in combination with optical parametric oscillators are too complex and thus bulky and expensive. In contrast, quantum cascade lasers (QCLs) constitute a class of very compact and robust semiconductor-based lasers, which are able to cover the mentioned wavelength range using the same semiconductor material system. In this tutorial, a brief review will be given on the state-of-the-art of QCL technology. Special emphasis will be addressed on QCL variants with well-defined spectral properties and spectral tunability. As an example for the use of wavelength agile QCL for active hyperspectral imaging, stand-off detection of explosives based on imaging backscattering laser spectroscopy will be discussed.
Yang, Quankui; Fuchs, Frank; Wagner, Joachim
Extracting surface land-cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic tasks is to identify and map surface water boundaries. Spectral water indexes have been successfully used in the extraction of water bodies in multispectral images. However, directly applying a water index method to hyperspectral images disregards the abundant spectral information and involves difficulty in selecting appropriate spectral bands. It is also a challenge for a spectral water index to distinguish water from shadowed regions. The purpose of this study is therefore to develop an index that is suitable for water extraction by the use of hyperspectral images, and with the capability to mitigate the effects of shadow and low-albedo surfaces, especially in urban areas. Thus, we introduce a new hyperspectral difference water index (HDWI) to improve the water classification accuracy in areas that include shadow over water, shadow over other ground surfaces, and low-albedo ground surfaces. We tested the new method using PHI-2, HyMAP, and ROSIS hyperspectral images of Shanghai, Munich, and Pavia. The performance of the water index was compared with the normalized difference water index (NDWI) and the Mahalanobis distance classifier (MDC). With all three test images, the accuracy of HDWI was significantly higher than that of NDWI and MDC. Therefore, HDWI can be used for extracting water with a high degree of accuracy, especially in urban areas, where shadow caused by high buildings is an important source of classification error.
Xie, Huan; Luo, Xin; Xu, Xiong; Tong, Xiaohua; Jin, Yanmin; Pan, Haiyan; Zhou, Bingzhong
We describe a novel digital light processing, DLP hyperspectral imaging system for visualizing chemical composition of in vivo tissues during surgical procedures non-invasively and at near video rate. The novelty of the DLP hyperspectral imaging system resides in (1) its ability to conform light to rapidly sweep through a series of preprogrammed spectral illuminations as simple as a set of contiguous bandpasses to any number of complex spectra, and (2) processing the reflected spectroscopic image data using unique supervised and unsupervised chemometric methods that color encode molecular content of tissue at each image detector pixel providing an optical biopsy. Spectral illumination of tissue is accomplished utilizing a DLPÂ® based spectral illuminator incorporating a series of bandpass spectra and measuring the reflectance image with a CCD array detector. Wavelength dependent images are post processed with a multivariate least squares analysis method using known reference spectra of oxy- and deoxyhemoglobin. Alternatively, illuminating with complex reference spectra reduces the number of spectral images required for generating chemically relevant images color encoded for relative percentage of oxyhemoglobin are collected and displayed in real time near-video rate, (3 to 4) frames per second (fps). As a proof of principle application, a kidney of an anesthetized pig was imaged before and after renal vasculature occlusion showing the clamped kidney to be 61% of the unclamped kidney percentage of oxyhemoglobin. Using the "3-Shot" spectral illumination method and gathering data at (3 to 4) fps shows a non-linear exponential de-oxygenation of hemoglobin reaching steady state within 30 seconds post occlusion.
Zuzak, Karel J.; Francis, Robert P.; Wehner, Eleanor F.; Smith, Jack; Litorja, Maritoni; Allen, David W.; Tracy, Chad; Cadeddu, Jeffrey; Livingston, Edward
1 Coded Hyperspectral Imaging and Blind Compressive Sensing Ajit Rajwade, David Kittle, Tsung of hyperspectral data imaged by a coded aperture camera. The measurements are manifested as a superposition reconstructing large hyperspectral datacubes. Comparisons are made between the proposed algorithm and other
Surgical technology advances slowly and only when there is overwhelming need for change. Change is resisted by surgeons and is made hard by FDA rules that inhibit innovation. There is a pressing need to improve surgeon's visualization of the operative field during laparoscopic surgery to minimize the risk for significant injury that can occur when surgeons are operating around delicate, hidden structures. We propose to use a Digital Light Processor-based hyperspectral imaging system to assist an operating surgeon's ability to see through tissues and identify otherwise hidden structures such as bile ducts during laparoscopic cholecystectomy.
Livingston, Edward H.
In this paper we present an implementation of the image compression routine SPIHT in reconfigurable logic. A discussion on why adaptive logic is required, as opposed to an ASIC, is provided along with background material on the image compression algorithm. We analyzed several Discrete Wavelet Transform architectures and selected the folded DWT design. In addition we provide a study on
Thomas W. Fry; Scott Hauck
this paper presents compression algorithm of multispectral image. At first multispectral image is converted to N-dimensional space vector, and then using PGK clustering algorithm to cluster compression, Experiments show that this algorithm with the detection vegetation can achieve the purpose of high compression ratio, low algorithm complexity, restored
Xu Su; Tan Xue; Chen Shanxue
In this paper, we propose an advanced hyperspectral video imaging system (AHVIS), which consists of an objective lens, an occlusion mask, a relay lens, an Amici prism and two cameras. An RGB camera is used for spatial reading and a gray scale camera is used for measuring the scene with spectral information. The objective lens collects more light energy from the observed scene and images the scene on an occlusion mask, which subsamples the image of the observed scene. Then, the subsampled image is sent to the gray scale camera through the relay lens and the Amici prism. The Amici prism that is used to realize spectral dispersion along the optical path reduces optical distortions and offers direct view of the scene. The main advantages of the proposed system are improved light throughput and less optical distortion. Furthermore, the presented configuration is more compact, robust and practicable. PMID:25321019
Feng, Jiao; Fang, Xiaojing; Cao, Xun; Ma, Chenguang; Dai, Qionghai; Zhu, Hongbo; Wang, Yongjin
A three-dimensional Gabor filter was developed for classification of hyperspectral remote sensing image. This method is based on the characteristics of hyperspectral image and the principle of texture extraction with 2-D Gabor filters. Three-dimensional Gabor filter is able to filter all the bands of hyperspectral image simultaneously, capturing the specific responses in different scales, orientations, and spectral-dependent properties from enormous image information, which greatly reduces the time consumption in hyperspectral image texture extraction, and solve the overlay difficulties of filtered spectrums. Using the designed three-dimensional Gabor filters in different scales and orientations, Hyperion image which covers the typical area of Qi Lian Mountain was processed with full bands to get 26 Gabor texture features and the spatial differences of Gabor feature textures corresponding to each land types were analyzed. On the basis of automatic subspace separation, the dimensions of the hyperspectral image were reduced by band index (BI) method which provides different band combinations for classification in order to search for the optimal magnitude of dimension reduction. Adding three-dimensional Gabor texture features successively according to its discrimination to the given land types, supervised classification was carried out with the classifier support vector machines (SVM). It is shown that the method using three-dimensional Gabor texture features and BI band selection based on automatic subspace separation for hyperspectral image classification can not only reduce dimensions; but also improve the classification accuracy and efficiency of hyperspectral image. PMID:25474965
Feng, Xiao; Xiao, Peng-feng; Li, Qi; Liu, Xiao-xi; Wu, Xiao-cui
A three-dimensional Gabor filter was developed for classification of hyperspectral remote sensing image. This method is based on the characteristics of hyperspectral image and the principle of texture extraction with 2-D Gabor filters. Three-dimensional Gabor filter is able to filter all the bands of hyperspectral image simultaneously, capturing the specific responses in different scales, orientations, and spectral-dependent properties from enormous image information, which greatly reduces the time consumption in hyperspectral image texture extraction, and solve the overlay difficulties of filtered spectrums. Using the designed three-dimensional Gabor filters in different scales and orientations, Hyperion image which covers the typical area of Qi Lian Mountain was processed with full bands to get 26 Gabor texture features and the spatial differences of Gabor feature textures corresponding to each land types were analyzed. On the basis of automatic subspace separation, the dimensions of the hyperspectral image were reduced by band index (BI) method which provides different band combinations for classification in order to search for the optimal magnitude of dimension reduction. Adding three-dimensional Gabor texture features successively according to its discrimination to the given land types, supervised classification was carried out with the classifier support vector machines (SVM). It is shown that the method using three-dimensional Gabor texture features and BI band selection based on automatic subspace separation for hyperspectral image classification can not only reduce dimensions; but also improve the classification accuracy and efficiency of hyperspectral image. PMID:25508744
Feng, Xiao; Xiao, Peng-feng; Li, Qi; Liu, Xiao-xi; Wu, Xiao-cui
An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data. PMID:23545982
August, Yitzhak; Vachman, Chaim; Rivenson, Yair; Stern, Adrian
This paper proposes a novel hyperspectral matching technique by integrating the Jeffries-Matusita measure (JM) and the Spectral Angle Mapper (SAM) algorithm. The deterministic Spectral Angle Mapper and stochastic Jeffries-Matusita measure are orthogonally projected using the sine and tangent functions to increase their spectral ability. The developed JM-SAM algorithm is implemented in effectively discriminating the landcover classes and cover types in the hyperspectral images acquired by PROBA/CHRIS and EO-1 Hyperion sensors. The reference spectra for different land-cover classes were derived from each of these images. The performance of the proposed measure is compared with the performance of the individual SAM and JM approaches. From the values of the relative spectral discriminatory probability (RSDPB) and relative discriminatory entropy value (RSDE), it is inferred that the hybrid JM-SAM approach results in a high spectral discriminability than the SAM and JM measures. Besides, the use of the improved JM-SAM algorithm for supervised classification of the images results in 92.9% and 91.47% accuracy compared to 73.13%, 79.41%, and 85.69% of minimum-distance, SAM and JM measures. It is also inferred that the increased spectral discriminability of JM-SAM measure is contributed by the JM distance. Further, it is seen that the proposed JM-SAM measure is compatible with varying spectral resolutions of PROBA/CHRIS (62 bands) and Hyperion (242 bands).
Padma, S.; Sanjeevi, S.
The new generation of hyperspectral sensors can provide images with a high spectral and spatial resolution. Recent improvements in mathematical morphology have developed new techniques such as the Attribute Profiles (APs) and the Extended Attribute Profiles (EAPs) that can effectively model the spatial information in remote sensing images. The main drawbacks of these techniques is the selection of the optimal range of values related to the family of criteria adopted to each filter step, and the high dimensionality of the profiles, which results in a very large number of features and therefore provoking the Hughes phenomenon. In this work, we focus on addressing the dimensionality issue, which leads to an highly intrinsic information redundancy, proposing a novel strategy for extracting spatial information from hyperspectral images based on the analysis of the Differential Attribute Profiles (DAPs). A DAP is generated by computing the derivative of the AP; it shows at each level the residual between two adjacent levels of the AP. By analyzing the multilevel behavior of the DAP, it is possible to extract geometrical features corresponding to the structures within the scene at different scales. Our proposed approach consists of two steps: 1) a homogeneity measurement is used to identify the level L in which a given pixel belongs to a region with a physical meaning; 2) the geometrical information of the extracted regions is fused into a single map considering their level L previously identified. The process is repeated for different attributes building a reduced EAP, whose dimensionality is much lower with respect to the original EAP ones. Experiments carried out on the hyperspectral data set of Pavia University area show the effectiveness of the proposed method in extracting spatial features related to the physical structures presented in the scene, achieving higher classification accuracy with respect to the ones reported in the state-of-the-art literature
Falco, Nicola; Benediktsson, Jon A.; Bruzzone, Lorenzo
This work focuses on an assessment of quality parameters characterizing a hyperspectral image collected by a new-generation high-resolution sensor named Hyper-SIMGA, which is a spectrometer operating in the push-broom configuration. By resorting to Shannon's information theory, the concept of quality is related to the information conveyed to a user by the hyperspectral data, which can be objectively defined from both the signal-to-noise ratio (SNR) and the mutual information between the unknown noise-free digitized signal and the corresponding noise-affected observed digital samples. The estimation of the mutual information has been exploited by resorting to a lossless data compression of the dataset. In fact, the bit-rate achieved by the reversible compression process is a suitable approximation of the decorrelated data entropy, which takes into account both the contribution of the "observation" noise, i.e. information regarded as statistical uncertainty, whose relevance is null to a user, and the intrinsic information of hypothetically noise-free samples. Noise estimation can be obtained once a suitable parametric model of the noise, assumed to be possibly non-Gaussian, has been preliminarily determined. Noise amplitude has been assessed by means of two independent estimators relying on two automatic procedures based on a scatterplot method and a bit-plane algorithm. Noise autocorrelation has been taken into account on the three allowed directions of the available data-volume. Results are reported and discussed employing a hyperspectral image (768 spectral bands) recorded by the new Hyper-SIMGA imaging spectrometer.
Aiazzi, Bruno; Alparone, Luciano; Barducci, Alessandro; Baronti, Stefano; Guzzi, Donatella; Marcoionni, Paolo; Pippi, Ivan; Selva, Massimo
The present report evaluated ultraviolet radiation (UVR) effects on the spectral signature of mycotoxin producing fungus Aspergillus flavus (A. flavus). Ultraviolet radiation has long been used to reduce microbe contamination and to inactivate mold spores. In view of the known effects of UVR on microorganisms, and because certain spectral bands in the signature of some fungi may be in the UV range, it is important to know the maximum acceptable limit of UVR exposure that does not significantly alter the fungal spectral signature and affect detection accuracy. A visible-near-infrared (VNIR) hyperspectral imaging system using focal plane pushbroom scanning for high spatial and spectral resolution imaging was utilized to detect any changes. A. flavus cultures were grown for 5 days and imaged after intermittent or continuous UVR treatment. The intermittent group was treated at 1-minute intervals for 10 minutes, and VNIR images were taken after each UVR treatment. The continuous group was irradiated for 10 minutes and imaged before and after treatment. A control sample group did not undergo UVR treatment, but was also imaged at 1-minute intervals for 10 minutes in the same manner as the intermittent group. Before and after UVR treatment, mean fungal sample reflectance was obtained through spatial subset of the image along with standard deviation and pre- and post-treatment reflectance was compared for each sample. Results show significant difference between the reflectances of treated and control A. flavus cultures after 10 min of UV radiation. Aditionally, the results demonstrate that even lethal doses of UVR do not immediately affect the spectral signature of A. flavus cultures suggesting that the excitation UV light source used in the present experiment may be safe to use with the UV hyperspectral imaging system when exposure time falls below 10 min.
Hruska, Zuzana; Yao, Haibo; DiCrispino, Kevin; Brabham, Kori; Lewis, David; Beach, Jim; Brown, Robert L.; Cleveland, Thomas E.
The presence of pits in processed cherry products causes safety concerns for consumers and imposes potential liability for the food industry. The objective of this research was to investigate a hyperspectral transmission imaging technique for detecting the pit in tart cherries. A hyperspectral imaging system was used to acquire transmission images from individual cherry fruit for four orientations before and after pits were removed over the spectral region between 450 nm and 1,000 nm. Cherries of three size groups (small, intermediate, and large), each with two color classes (light red and dark red) were used for determining the effect of fruit orientation, size, and color on the pit detection accuracy. Additional cherries were studied for the effect of defect (i.e., bruises) on the pit detection. Computer algorithms were developed using the neural network (NN) method to classify the cherries with and without the pit. Two types of data inputs, i.e., single spectra and selected regions of interest (ROIs), were compared. The spectral region between 690 nm and 850 nm was most appropriate for cherry pit detection. The NN with inputs of ROIs achieved higher pit detection rates ranging from 90.6% to 100%, with the average correct rate of 98.4%. Fruit orientation and color had a small effect (less than 1%) on pit detection. Fruit size and defect affected pit detection and their effect could be minimized by training the NN with properly selected cherry samples.
Qin, Jianwei; Lu, Renfu
Spectral unmixing technique is used in remote sensed data analysis for the determination of certain basis spectra called 'endmembers'. Once those spectra are found, the image cube can be 'unmixed' into fractional abundance of each material in each pixel. In the present work infrared spectra recorded by Infrared Atmospheric Sounding Interferometer (IASI) were used to characterize the emission from Grimsvotn volcanic eruption on 2011. In particular, a methodology based on spectral unmixing theory was used in order to extract the spectral signature of volcanic cloud constituents, such as ash and sulphur dioxide (SO2) and maps of their abundances in a IASI image were obtained. Taking the advantage of IASI broad spectral coverage the broadband signature in the Thermal Infrared (TIR) radiance spectra in the 1000-1410 cm-1 range associated with the presence of aerosols was obtained. Volcanic ash and SO2 spectral signatures were extracted, as well as those related to the simultaneous presence of ash, SO2 and cloud. The study proved that spectral unmixing, applied to Hyperspectral images, is able to identify volcanic aerosols and other species like SO2 despite a strong presence of meteorological clouds. Moreover, the analysis of hyperspectral datasets permitted to generate abundance maps for each endmember extracted. In particular, maps obtained for the test case of 2011 May, 23th put in evidence the separation between clouds of ejected SO2 and volcanic ash. The former dispersed at Northern latitudes, whilst the latter was situated at southern latitudes, South of Iceland.
Piscini, Alessandro; Carboni, Elisa; Del Frate, Fabio; Grainger, Roy Gordon
Polarimetric hyperspectral imaging (P-HSI) has the potential to improve target detection, material identification, and background characterization over conventional hyperspectral imaging and polarimetric imaging. To fully exploit the spectro-polarimetric signatures captured by such an instrument, a careful calibration process is required to remove the spectrally- and polarimetrically-dependent system response (gain). Calibration of instruments operating in the long-wave infrared (LWIR, 8?m to 12 ?m) is further complicated by the polarized spectral radiation generated within the instrument (offset). This paper presents a calibration methodology developed for a LWIR Telops Hyper-Cam modified for polarimetry by replacing the entrance window with a rotatable holographic wire-grid polarizer (4000 line/mm, ZnSe substrate, 350:1 extinction ratio). A standard Fourier-transform spectrometer (FTS) spectro-radiometric calibration is modified to include a Mueller-matrix approach to account for polarized transmission through and polarized selfemission from each optical interface. It is demonstrated that under the ideal polarizer assumption, two distinct blackbody measurements at polarizer angles of 0°, 45°, 90°, and 135° are sufficient to calibrate the system for apparent degree-of-linear-polarization (DoLP) measurements. Noise-equivalent s1, s2, and DoLP are quantified using a wide-area blackbody. A polarization-state generator is used to determine the Mueller deviation matrix. Finally, a realistic scene involving buildings, cars, sky radiance, and natural vegetation is presented.
Holder, Joel G.; Martin, Jacob A.; Pitz, Jeremey; Pezzaniti, Joseph L.; Gross, Kevin C.
DLP® hyperspectral reflectance imaging in the visible range has been previously shown to quantify hemoglobin oxygenation in subsurface tissues, 1 mm to 2 mm deep. Extending the spectral range into the near infrared reflects biochemical information from deeper subsurface tissues. Unlike any other illumination method, the digital micro-mirror device, DMD, chip is programmable, allowing the user to actively illuminate with precisely predetermined spectra of illumination with a minimum bandpass of approximately 10 nm. It is possible to construct active spectral-based illumination that includes but is not limited to containing sharp cutoffs to act as filters or forming complex spectra, varying the intensity of light at discrete wavelengths. We have characterized and tested a pure NIR, 760 nm to 1600 nm, DLP hyperspectral reflectance imaging system. In its simplest application, the NIR system can be used to quantify the percentage of water in a subject, enabling edema visualization. It can also be used to map vein structure in a patient in real time. During gall bladder surgery, this system could be invaluable in imaging bile through fatty tissue, aiding surgeons in locating the common bile duct in real time without injecting any contrast agents.
Wehner, Eleanor; Thapa, Abhas; Livingston, Edward; Zuzak, Karel
In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost. PMID:22453490
Liang, Yuan; Li, Jianping; Guo, Ke
Endmember extraction and abundances calculation represent critical steps within the process of linearly unmixing a given hyperspectral image because of two main reasons. The first one is due to the need of computing a set of accurate endmembers in order to further obtain confident abundance maps. The second one refers to the huge amount of operations involved in these time-consuming processes. This work proposes an algorithm to estimate the endmembers of a hyperspectral image under analysis and its abundances at the same time. The main advantage of this algorithm is its high parallelization degree and the mathematical simplicity of the operations implemented. This algorithm estimates the endmembers as virtual pixels. In particular, the proposed algorithm performs the descent gradient method to iteratively refine the endmembers and the abundances, reducing the mean square error, according with the linear unmixing model. Some mathematical restrictions must be added so the method converges in a unique and realistic solution. According with the algorithm nature, these restrictions can be easily implemented. The results obtained with synthetic images demonstrate the well behavior of the algorithm proposed. Moreover, the results obtained with the well-known Cuprite dataset also corroborate the benefits of our proposal.
Guerra, Raúl; López, Sebastián.; Callico, Gustavo M.; López, Jose F.; Sarmiento, Roberto
In the context of precision agriculture, several recent studies have focused on detecting crop stress caused by pathogenic fungi. For this purpose, several sensor systems have been used to develop in-field-detection systems or to test possible applications of remote sensing. The objective of this research was to evaluate the potential of different sensor systems for multitemporal monitoring of leaf rust (puccinia recondita) infected wheat crops, with the aim of early detection of infected stands. A comparison between a hyperspectral (120 spectral bands) and a multispectral (3 spectral bands) imaging system shows the benefits and limitations of each approach. Reflectance data of leaf rust infected and fungicide treated control wheat stand boxes (1sqm each) were collected before and until 17 days after inoculation. Plants were grown under controlled conditions in the greenhouse and measurements were taken under consistent illumination conditions. The results of mixture tuned matched filtering analysis showed the suitability of hyperspectral data for early discrimination of leaf rust infected wheat crops due to their higher spectral sensitivity. Five days after inoculation leaf rust infected leaves were detected, although only slight visual symptoms appeared. A clear discrimination between infected and control stands was possible. Multispectral data showed a higher sensitivity to external factors like illumination conditions, causing poor classification accuracy. Nevertheless, if these factors could get under control, even multispectral data may serve a good indicator for infection severity.
Franke, Jonas; Menz, Gunter; Oerke, Erich-Christian; Rascher, Uwe
\\u000aHyperspectral imaging (HSI) is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool for non-destructive food analysis. This paper provides an introduction to hyperspectral imaging: HSI equipment, image acquisition and processing
A. A. Gowen; C. P. O'Donnell; P. J. Cullen; G. Downey; J. M. Frias
In this paper, the 3-D wavelet-fractal coding was used to compress the hyperspectral remote sensing image. The classical eight kinds of affine transformations in 2-D fractal image compression were generalized to nineteen for the 3-D fractal image compression. Hyperspectral image date cube was first translated by 3-D wavelet and then the 3-D fractal compression coding was applied to lowest frequency
Pan Wei; Zou Yi; Ao Lu
A novel hyperspectral imaging system (HI90, Bruker Optics), working in the mid-infrared range and recently developed for the remote identification and mapping of hazardous compounds, has here been optimized for investigating painting surfaces. The painting Sestante 10 (1982) by Alberto Burri has been spectrally and spatially investigated with the HI90 system revealing the distribution of inorganic materials constituting the artworks. In order to validate the results obtainable by the imager for the pigment identification previous tests on laboratory models were performed. Yellow, white and blue pigments painted with different binders (namely egg, alkyd, acrylic and vinyl) were investigated by the HI90. Afterwards, the polychrome painting Sestante 10 was investigated focusing the attention on the inorganic material distribution revealing the presence of different extenders (kaolin, BaSO4, CaSO4) mixed with the various silica-based pigments present in the painting. The brightness temperature spectra collected by HI90 have also been compared to single point reflection spectra acquired by a conventional portable FTIR spectrometer (Alpha-R by Bruker Optics) highlighting the good spectral quality of the imaging system. This comparison permitted also to evaluate the spectral response and the diagnostic strengths of the spectral range available by the HI90 imaging (1300-860 cm-1), validating the reliability of the obtained chemical images. This study clearly highlights the high potential of the new hyperspectral imaging system and opens up new perspectives in the current scientific interest devoted to the application of mapping and imaging methods for the study of painting surfaces.
Rosi, Francesca; Harig, Roland; Miliani, Costanza; Braun, René; Sali, Diego; Daveri, Alessia; Brunetti, Brunetto G.; Sgamellotti, Antonio
Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for
Emmanuel Christophe; Corinne Mailhes; Pierre Duhamel
A MULTI-MODAL PATTERN CLASSIFICATION FRAMEWORK FOR HYPERSPECTRAL IMAGE ANALYSIS Wei Li, Saurabh Analysis (LFDA)  to reduce the dimensionality of the data while preserving its multi- modal structure reduction is a crucial preprocessing step for effective analysis of high dimensional hyperspectral imagery
Fowler, James E.
This paper reports on the development of a hyperspectral imaging prototype for evaluation of external and internal quality of pickling cucumbers. The prototype consisted of a two-lane round belt conveyor, two illumination sources (one for reflectance and one for transmittance), and a hyperspectral i...
A new, easy-to-implement approach for achieving highly accurate spectral and radiometric calibration of array-based, hyperspectral pushbroom imagers is presented in this paper. The equivalence of the plane of the exit port of an integrating sphere to a Lambertian surface is utilized to provide a field-filling radiance source for the imager. Several different continuous wave lasers of various wavelengths and a quartz-tungsten-halogen lamp internally illuminate the sphere. The imager is positioned to "stare" into the port, and the resultant data cube is analyzed to determine wavelength calibrations, spectral widths of channels, radiometric characteristics, and signal-to-noise ratio, as well as an estimate of signal-to-noise performance in the field. The "smile" (geometric distortion of spectra) of the system can be quickly ascertained using this method. As the price and availability of solid state laser sources improve, this technique could gain wide acceptance.
Ryan, Robert; Olive, Dan; ONeal, Duane; Schere, Chris; Nixon, Thomas; May, Chengye; Ryan, Jim; Stanley, Tom; Witcher, Kern
This paper analyzes the feasibility and performance of HSI systems for medical diagnosis as well as for food safety. Illness prevention and early disease detection are key elements for maintaining good health. Health care practitioners worldwide rely on innovative electronic devices to accurately identify disease. Hyperspectral imaging (HSI) is an emerging technique that may provide a less invasive procedure than conventional diagnostic imaging. By analyzing reflected and fluorescent light applied to the human body, a HSI system serves as a diagnostic tool as well as a method for evaluating the effectiveness of applied therapies. The safe supply and production of food is also of paramount importance to public health illness prevention. Although this paper will focus on imaging and spectroscopy in food inspection procedures -- the detection of contaminated food sources -- to ensure food quality, HSI also shows promise in detecting pesticide levels in food production (agriculture.)
Carrasco, Oscar; Gomez, Richard B.; Chainani, Arun; Roper, William E.
Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost. PMID:22823350
Feng, Yao-Ze; Sun, Da-Wen
Hyperspectral image contains fine spectral and spatial resolutions for generating accurate land use and land cover maps. Supervised classification is the one of method used to exploit the information from the hyperspectral image. The traditional supervised classification methods could not be able to overcome the limitations of the hyperspectral image. The multiple classifier system (MCS) has the potential to increase the classification accuracy and reliability of the hyperspectral image. However, the MCS extracts only the spectral information from the hyperspectral image and neglects the spatial contextual information. Incorporating spatial contextual information along with spectral information is necessary to obtain smooth classification maps. Our objective of this paper is to design a methodology to fully exploit the spectral and spatial information from the hyperspectral image for land cover classification using MCS and Graph cut (GC) method. The problem is modelled as the energy minimization problem and solved using ?-expansion based graph cut method. Experiments are conducted with two hyperspectral images and the result shows that the proposed MCS based graph cut method produces good quality classification map.
Bhushan, D. B.; Nidamanuri, R. R.
NASA's satellites currently do not make use of advanced image compression techniques during data transmission to earth because of limitations in the available platforms. With the advent of Field Programmable Gate Arrays (FPGAs) and Adaptive Computing technologies it is now possible to construct a system, which compresses the data stream before down linking. Our work is part of a NASA-sponsored
Thomas W. Fry; Scott Hauck
An innovative procedure to classify oat and groat kernels based on coupling hyperspectral imaging (HSI) in the near infrared (NIR) range (1006-1650 nm) and chemometrics was designed, developed and validated. According to market requirements, the amount of groat, that is the hull-less oat kernels, is one of the most important quality characteristics of oats. Hyperspectral images of oat and groat samples have been acquired by using a NIR spectral camera (Specim, Finland) and the resulting data hypercubes were analyzed applying Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares-Discriminant Analysis (PLS-DA) to build the classification models to discriminate the two kernel typologies. Results showed that it is possible to accurately recognize oat and groat single kernels by HSI (prediction accuracy was almost 100%). The study demonstrated also that good classification results could be obtained using only three wavelengths (1132, 1195 and 1608 nm), selected by means of a bootstrap-VIP procedure, allowing to speed up the classification processing for industrial applications. The developed objective and non-destructive method based on HSI can be utilized for quality control purposes and/or for the definition of innovative sorting logics of oat grains. PMID:23200388
Serranti, Silvia; Cesare, Daniela; Marini, Federico; Bonifazi, Giuseppe
Over the course of the last several years hyperspectral imaging (HSI) has seen increased usage in biomedicine. Within the medical field in particular HSI has been recognized as having the potential to make an immediate impact by reducing the risks and complications associated with laparotomies (surgical procedures involving large incisions into the abdominal wall) and related procedures. There are several ongoing studies focused on such applications. Hyperspectral images were acquired during pancreatoduodenectomies (commonly referred to as Whipple procedures), a surgical procedure done to remove cancerous tumors involving the pancreas and gallbladder. As a result of the complexity of the local anatomy, identifying where the common bile duct (CBD) is can be difficult, resulting in comparatively high incidents of injury to the CBD and associated complications. It is here that HSI has the potential to help reduce the risk of such events from happening. Because the bile contained within the CBD exhibits a unique spectral signature, we are able to utilize HSI segmentation algorithms to help in identifying where the CBD is. In the work presented here we discuss approaches to this segmentation problem and present the results.
Samarov, Daniel; Wehner, Eleanor; Schwarz, Roderich; Zuzak, Karel; Livingston, Edward
This paper is concerned with the detection of bone fragments embedded in compressed de-boned skinless chicken breast fillets\\u000a by enhancing single-band transmittance images generated by back-lighting and exploiting spectral information from hyperspectral\\u000a reflectance images. Optical imaging of chicken fillets is often dominated by multiple scattering properties of the fillets.\\u000a Thus, resulting images from multiple scattering are diffused, scattered and low
Seung Chul Yoon; Kurt C. Lawrence; Douglas P. Smith; William R. Windham
Hyperspectral Imaging is used in many applications to identify or analyze materials in a scene based on the materials' spectral signatures. Unique features in the spectral signatures can span beyond the spectral range of the hyperspectral imager. Additionally, lighting conditions and other factors can adversely affect the quality of data. Expanding the spectral range of hyperspectral imaging systems can therefore improve the accuracy of object/material recognition/analysis by allowing the system to "see" more of the spectral signatures as well as expand the number of objects/materials in a scene that can be identified/analyzed. This is particularly important in applications where erroneous identification or analysis can result in substantial risk or cost. More and more users are using two (or more) hyperspectral imagers to obtain different spectral ranges for their applications. Very few are effectively combining the data from the different hyperspectral imagers because it would require the hyperspectral imagers to be operated under tightly controlled conditions and the process of pixel coregistration is a very tedious and problematic post-processing step. In addition, this post-processing step prevents the use of the combined data in real-time applications. This paper describes a co-boresighted Vis-NIR and SWIR hyperspectral imaging system which Headwall Photonics is currently developing. It integrates two hyperspectral imagers, each optimized for its respective spectral range, into a single system with real-time pixel co-registration resulting in a system capable of producing wide-spectrum hyperspectral images with high spectral resolution. Aside from enabling real-time wide spectrum applications, such a system significantly simplifies the data acquisition and analysis for the user.
"Superpixel segmentation" is a novel approach to facilitate statistical analyses of hyperspectral image data with high spatial resolution and subtle spectral features. The method oversegments the image into homogeneous regions each comprised of several contiguous pixels. This can reduce noise by exploiting scene features' spatial contiguity: isolated spectral features are likely to be noise, but spectral features that appear in several adjacent pixels probably indicate real materials in the scene. The mean spectra from each superpixel define a smaller, noise-reduced dataset. This preprocessing step improves endmember detection for the images in our study. Our endmember detection approach presumes a linear (geographic) mixing model for image spectra. We generate superpixels with the Felzenszwalb/Huttenlocher graph-based segmentation  with a Euclidean distance metric. This segmentation shatters the image into thousands of superpixels, each with an area of approximately 20 image pixels. We then apply Symmetric Maximum Angle Convex Cone (SMACC) endmember detection algorithm to the data set consisting of the mean spectrum from all superpixels. We evaluated the approach for several images from the Compact Reconnaissance Imaging Spectrometer (CRISM) . We used the 1000-2500nm wavelengths of images frt00003e12 and frt00003fb9. We cleaned the images with atmospheric correction based on Olympus Mons spectra  and preprocessed with a radius-1 median filter in the spectral domain. Endmembers produced with and without the superpixel reduction are compared to the representative (mean) spectra of five representative mineral classes identified in an expert analysis of each scene. Expert-identified minerals include mafic minerals and phyllosilicate deposits that in some cases subtended just a few tens of pixels. Only the endmembers from the superpixel approach reflected all major mineral constituents in the images. Additionally, the superpixel endmembers are more quantitatively more faithful to the mean mineral spectra, reducing average squared error over all wavelengths for comparisons between endmembers and the closest-matching mineral spectrum (Figures). We conclude that superpixel segmentation holds promise for improving signal-to-noise for statistical analyses of hyperspectral images with high spatial resolution. References  P.F. Felzenszwalb and D.P. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of Computer Vision, vol. 59:2, pp. 167-181, 2004.  S. Murchie et al., “Compact reconnaissance imaging spectrometer for Mars (CRISM) on Mars reconnaissance orbiter,” J. Geophys. Res, vol. 112, no. 5, 2007.  F. Morgan et al., “CAT tutorial,” CRISM Data User’s Workshop, Lunar and Planetary Science Conference 2009.
Thompson, D. R.; de Granville, C.; Gilmore, M. S.; Castano, R.
Hyperspectral sensors are passive sensors that simultaneously record images for hundreds of contiguous and narrowly spaced regions of the electromagnetic spectrum. Each image corresponds to the same ground scene, thus creating a cube of images that contain both spatial and spectral information about the objects and backgrounds in the scene. In this paper, we present an adaptive anomaly detector designed
Susan M. Schweizer; José M. F. Moura
Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a manner similar to that of a baseline hyperspectral- image-compression method. The mean values are encoded in the compressed bit stream and added back to the data at the appropriate decompression step. The overhead incurred by encoding the mean values only a few bits per spectral band is negligible with respect to the huge size of a typical hyperspectral data set. The other method is denoted modified decomposition. This method is so named because it involves a modified version of a commonly used multiresolution wavelet decomposition, known in the art as the 3D Mallat decomposition, in which (a) the first of multiple stages of a 3D wavelet transform is applied to the entire dataset and (b) subsequent stages are applied only to the horizontally-, vertically-, and spectrally-low-pass subband from the preceding stage. In the modified decomposition, in stages after the first, not only is the spatially-low-pass, spectrally-low-pass subband further decomposed, but also spatially-low-pass, spectrally-high-pass subbands are further decomposed spatially. Either method can be used alone to improve the quality of a reconstructed image (see figure). Alternatively, the two methods can be combined by first performing modified decomposition, then subtracting the mean values from spatial planes of spatially-low-pass subbands.
Klimesh, Matthew; Kiely, Aaron; Xie, Hua; Aranki, Nazeeh
Estimates of vegetation water content are of great interest for assessing vegetation water status in agriculture and forestry, and have been used for drought assessment. This study focuses on the retrieval of foliar water content with hyperspectral data at canopy level. The hyperspectral image used in this study was acquired by the airborne operative modular imaging spectrometer (OMIS) at Demonstration Site for Precision Agriculture in Xiaotangshan area, Beijing, on April 26th, 2001. 40 image spectra were extracted to correspond to the quasi-synchronous meansurements of foliar water content (FWC). The image spectra of winter wheat were utilized to validate the sensitivity of the existing and novel water indices and parameters of three water absorption features in NIR and SWIR regions. Correlation analysis showed that, NDWI(860,1241) and NDWI(860,1200) both had significant linear relationships with FWC (R2 were 0.4124 and 0.4042 respectively). Red edge position (REP) could reflect indirectly the variations of wheat FWC to some extent. Significant linear relationships were also found between WI(820,1600) and FWC, and between WI(900,1200) and FWC, while no relationship was shown between the traditional WI(900,970) and FWC. The derived depth of water absorption centered around 2078nm, namely AD2078, had the highest linear correlation with FWC (R2 is 0.4551) , much higher than those parameters derived from the two water absorption around 1175 and 1409. In the end, AD2078 was applied to OMIS image to map the foliar water content. The value range of the inverted foliar water content ranged from 69.39 to 78.35%, which was quite close to that of the field measurements (70.72-78.12%). The distribution of the FWC map was quite consistent with growth status of winter wheat.
Zhang, Xia; Jiao, Quanjun; Wu, Di; Zhang, Bing; Gao, Lianru
Hyperspectral imaging is a non-contact, non-destructive technique that combines spectroscopy and imaging to extract information\\u000a from a sample. This technology has recently emerged as a powerful technique for food analysis. In this study, the potential\\u000a of hyperspectral imaging (HSI) to predict white button mushroom moisture content (MC) was investigated. Mushrooms were subjected\\u000a to dehydration at 45 ± 1 °C for different time periods
Masoud Taghizadeh; Aoife Gowen; Colm P. O’Donnell
\\u000a As the dimensionality of remotely sensed data increases, the need for efficient compression algorithms for hyperspectral images\\u000a also increases. However, when hyperspectral images are compressed with conventional image compression algorithms, which have\\u000a been developed to minimize mean squared errors, discriminant information necessary to distinguish among classes may be lost\\u000a during compression process. In this paper, we propose to enhance such
C. Lee; E. Choi; J. Choe; T. Jeong
Accurate approximation of noise in hyperspectral (HS) images plays an important role in better visualization and image processing. Conventional algorithms often hypothesize the noise type to be either purely additive or of a mixed noise type for the signal-dependent (SD) noise component and the signal-independent (SI) noise component in HS images. This can result in application-driven algorithm design and limited use in different noise types. Moreover, as the highly textured HS images have abundant edges and textures, existing algorithms may fail to produce accurate noise estimation. To address these challenges, we propose a noise estimation algorithm that can adaptively estimate both purely additive noise and mixed noise in HS images with various complexities. First, homogeneous areas are automatically detected using a new region-growing-based approach, in which the similarity of two pixels is calculated by a robust spectral metric. Then, the mixed noise variance of each homogeneous region is estimated based on multiple linear regression technology. Finally, intensities of the SD and SI noise are obtained with a modified scatter plot approach. We quantitatively evaluated our algorithm on the synthetic HS data. Compared with the benchmarking and state-of-the-art algorithms, the proposed algorithm is more accurate and robust when facing images with different complexities. Experimental results with real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images further demonstrated the superiority of our algorithm. PMID:25402795
Fu, Peng; Li, Changyang; Xia, Yong; Ji, Zexuan; Sun, Quansen; Cai, Weidong; Feng, David Dagan
A three-dimensional (3D) wavelet coder based on 3D significance tree splitting is proposed for hyperspectral image compression. 3D discrete wavelet transform (DWT) is applied to explore the spatial and spectral correlations. Then the 3D significance tree structure is constructed in 3D wavelet domain, and wavelet coefficients are encoded via 3D significance tree splitting. This proposed algorithm does not need to use ordered lists, moreover it has less complexity and requires lower fixed memory than 3D set partitioning in hierarchical trees (SPIHT) algorithm and 3D set partitioned embedded block (SPECK) algorithm. The numerical experiments on AVIRIS images show that the proposed algorithm outperforms 3D SPECK, and has a minor loss of performance compared with 3D SPIHT. This algorithm is suitable for simple hardware implementation and can be applied to progressive transmission.
Huang, Jing; Zhu, Rihong; Li, Jianxin; He, Yong
Recent compressed sensing (CS) results show that it is possible to accurately reconstruct images from a small number of linear measurements via convex optimization techniques. In this paper, according to the correlation analysis of linear measurements for hyperspectral images, a joint sparsity reconstruction algorithm based on interband prediction and joint optimization is proposed. In the method, linear prediction is first applied to remove the correlations among successive spectral band measurement vectors. The obtained residual measurement vectors are then recovered using the proposed joint optimization based POCS (projections onto convex sets) algorithm with the steepest descent method. In addition, a pixel-guided stopping criterion is introduced to stop the iteration. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed.
Liu, Haiying; Li, Yunsong; Zhang, Jing; Song, Juan; Lv, Pei
Traditional airborne environmental monitoring has frequently deployed hyperspectral imaging as a leading tool for characterizing and analyzing a scene's critical spectrum-based signatures for applications in agriculture genomics and crop health, vegetation and mineral monitoring, and hazardous material detection. As the acceptance of hyperspectral evaluation grows in the airborne community, there has been a dramatic trend in moving the technology from use on midsize aircraft to Unmanned Aerial Systems (UAS). The use of UAS accomplishes a number of goals including the reduction in cost to run multiple seasonal evaluations over smaller but highly valuable land-areas, the ability to use frequent data collections to make rapid decisions on land management, and the improvement of spatial resolution by flying at lower altitudes (< 150 m). Despite this trend, there are several key parameters affecting the use of traditional hyperspectral instruments in UAS with payloads less than 0.5 kg (~1lb) where size, weight and power (SWaP) are critical to how high and how far a given UAS can fly. Additionally, on many of the light-weight UAS, users are frequently trying to capture data from one or more instruments to augment the hyperspectral data collection, thus reducing the amount of SWaP available to the hyperspectral instrumentation. The following manuscript will provide an analysis on a newly-developed miniaturized hyperspectral imaging platform that provides full hyperspectral resolution and traditional hyperspectral capabilities without sacrificing performance to accommodate the decreasing SWaP of smaller and smaller UAS platforms.
Hill, Samuel L.; Clemens, Peter
Citrus are one of the major fruit produced in China. Most of this production is exported to Europe for fresh consumption, where consumers increasingly demand best quality. Citrus canker is one of the most devastating diseases that threaten peel of most commercial citrus varieties. The aim of this research was to investigate the potential of using hyperspectral imaging technique for
Jiangbo Li; Xiuqin Rao; Junxian Guo; Yibin Ying
VTT Technical Research Centre of Finland has developed a Fabry-Perot Interferometer (FPI) based hyperspectral imager compatible with light weight UAV (Unmanned Aerial Vehicle) platforms (SPIE Proc. 74741, 8186B2). The FPI based hyperspectral imager was used in a UAV imaging campaign for forest and agriculture tests during the summer 2011 (SPIE Proc. 81743). During these tests high spatial resolution Color-Infrared (CIR) images and hyperspectral images were recorded on separate flights. The spectral bands of the CIR camera were 500 - 580 nm for the green band, 580 - 700 nm for the red band and 700 - 1000 nm for the near infrared band. For the summer 2012 flight campaign a new hyperspectral imager is currently being developed. A custom made CIR camera will also be used. The system which includes both the high spatial resolution Color-Infrared camera and a light weight hyperspectral imager can provide all necessary data with just one UAV flight over the target area. The new UAV imaging system contains a 4 Megapixel CIR camera which is used for the generation of the digital surface models and CIR mosaics. The hyperspectral data can be recorded in the wavelength range 500 - 900 nm at a resolution of 10 - 30 nm at FWHM. The resolution can be selected from approximate values of 10, 15, 20 or 30 nm at FWHM.
Mäkynen, Jussi; Saari, Heikki; Holmlund, Christer; Mannila, Rami; Antila, Tapani
ICER-3D is a progressive, wavelet-based compressor for hyperspectral images. ICER-3D is derived from the ICER image compressor. ICER-3D can provide lossless and lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The three-dimensional wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of hyperspectral data sets, while facilitating elimination of spectral ringing artifacts. Correlation is further exploited by a context modeler that effectively exploits spectral dependencies in the wavelet-transformed hyperspectral data. Performance results illustrating the benefits of these features are presented.
Kiely, A.; Klimesh, M.; Xie, H.; Aranki, N.
Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis collected on a daily basis into scientific understanding is critical for space-based Earth science analysis assume homogeneity in the computing platform, heterogeneous networks of computers represent
Plaza, Antonio J.
White mushrooms were subjected to mechanical injury by controlled shaking in a plastic box at 400 rpm for different times (0, 60, 120, 300 and 600 s). Immediately after shaking, hyperspectral images were obtained using two pushbroom line-scanning hyperspectral imaging instruments, one operating in the wavelength range of 400 - 1000 nm with spectroscopic resolution of 5 nm, the other operating in the wavelength range of 950 - 1700 nm with spectroscopic resolution of 7 nm. Different spectral and spatial pretreatments were investigated to reduce the effect of sample curvature on hyperspectral data. Algorithms based on Chemometric techniques (Principal Component Analysis and Partial Least Squares Discriminant Analysis) and image processing methods (masking, thresholding, morphological operations) were developed for pixel classification in hyperspectral images. In addition, correlation analysis, spectral angle mapping and scaled difference of sample spectra were investigated and compared with the chemometric approaches.
Gowen, A. A.; O'Donnell, C. P.
studies, wild land fire tracking, biological threat detection, monitoring of oil spills and other types's Goddard Space Flight Center. 1 Introduction Hyperspectral imaging is an emerging research area concerned
Plaza, Antonio J.
The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research. PMID:22823348
Elmasry, Gamal; Kamruzzaman, Mohammed; Sun, Da-Wen; Allen, Paul
A portable hyperspectral device (ASD-FieldSpec FR Pro) has been employed for the characterization of alterations affecting the marble facade of the Santa Maria Novella church (XIII cent.) in Florence (Italy). The ASD-FieldSpec FR Pro collects the reflectance spectra of a selected target area (about 1.5 cm2). The spectra of calcite, gypsum and other mineral phases commonly occurring on outdoor surfaces exposed to the urban atmosphere were collected and presented. The spectral features of alteration minerals (depth of reflectance minima) appear to be affected by grain size, phase abundance in addition to lightness (L*) of the target area. Notwithstanding these limitations, the spectra may be used for a qualitative screening of the alteration and, under reasonable assumptions, the reflectance band depth may be used also for quantitative estimation of phase abundance. The monitoring of the conservation state of outdoor surfaces is considered of fundamental importance to plan conservative interventions on historical buildings. Our results point out that portable hyperspectral instruments may be considered as powerful tools for characterizing historical surfaces in a nondestructive and noninvasive way.
Camaiti, M.; Vettori, S.; Benvenuti, M.; Chiarantini, L.; Costagliola, P.; Di Benedetto, F.; Moretti, S.; Paba, F.; Pecchioni, E.
Hyperspectral fluorescence imaging techniques were investigated for detection of two genera of microbial biofilms on stainless\\u000a steel material which is commonly used to manufacture food processing equipment. Stainless steel coupons were deposited in\\u000a nonpathogenic E. coli O157:H7 and Salmonella cultures, prepared using M9 minimal medium with casamino acids (M9C), for 6 days at 37 °C. Hyperspectral fluorescence emission\\u000a images of the biofilm formations
Won Jun; Moon S. Kim; Kangjin Lee; Patricia Millner; Kuanglin Chao
A new architecture for HIPAS (Hyperspectral Image Processing and Analysis System V2.0) was introduced in this paper which was modified and improved based on the first version of HIPAS V1.0. The comprehensive hyperspectral image analyzing system has been developed under VC++6.0 integrated development environment (IDE) and obtained perfect runtime efficiency and stability. The base architecture was specially designed and implemented
Jianlin Yu; Xingtang Hu; Bing Zhang; Shunian Ning
In this paper, we propose a hyperspectral image lossy-to-lossless compression coder based on the Three-Dimensional Embedded ZeroBlock Coding (3D EZBC) algorithm. This coder adopts the three-dimensional integer wavelet packet transform with unitary scaling to decorrelate and the 3D EZBC algorithm without motion compensation to process bitplane zeroblock coding. For hyperspectral image compression using the 3D EZBC algorithm, the lossy-to-lossless compression
Ying Hou; Guizhong Liu
Multispectral refractometers typically measure refractive index (RI) at discrete monochromatic wavelengths via a serial process. We report on the demonstration of a white light full field imaging based refractometer capable of instantaneous multispectral measurement of absolute RI of clear liquid/gel samples across the entire visible light spectrum. The broad optical bandwidth refractometer is capable of hyperspectral measurement of RI in the range 1.30 1.70 between 400nm 700nm with a maximum error of 0.0036 units (0.24% of actual) at 414nm for a = 1.50 sample. We present system design and calibration method details as well as results from a system validation sample.
Baba, Justin S [ORNL; Boudreaux, Philip R [ORNL
Nonnegative matrix factorization and its variants are powerful techniques for the analysis of hyperspectral images (HSI). Nonnegative matrix underapproximation (NMU) is a recent closely related model that uses additional underapproximation constraints enabling the extraction of features (e.g., abundance maps in HSI) in a recursive way while preserving nonnegativity. We propose to further improve NMU by using the spatial information: we incorporate into the model the fact that neighboring pixels are likely to contain the same materials. This approach thus incorporates structural and textural information from neighboring pixels. We use an l1-norm penalty term more suitable to preserving sharp changes, and solve the corresponding optimization problem using iteratively reweighted least squares. The effectiveness of the approach is illustrated with analysis of the real-world cuprite dataset.
Gillis, Nicolas; Plemmons, Robert J.; Zhang, Qiang
Detecting enemy's targets and being undetectable play increasingly important roles in modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. As supervised classification requires prior knowledge which cannot be acquired easily, unsupervised classification usually is adopted to process hyperspectral images to detect camouflaged target. But one of its drawbacks—low detecting accuracy confines its application for camouflaged target detecting. Most research on the processing of hyperspectral image tends to focus exclusively on spectral domain and ignores spatial domain. However current hyperspectral image provides high spatial resolution which contains useful information for camouflaged target detecting. A new method combining spectral and spatial information is proposed to increase the detecting accuracy using unsupervised classification. The method has two steps. In the first step, a traditional unsupervised classifier (i.e. K-MEANS, ISODATA) is adopted to classify the hyperspectral image to acquire basic classifications or clusters. During the second step, a 3×3 model and spectral angle mapping are utilized to test the spatial character of the hyperspectral image. The spatial character is defined as spatial homogeneity and calculated by spectral angle mapping. Theory analysis and experiment shows the method is reasonable and efficient. Camouflaged targets are extracted from the background and different camouflaged targets are also recognized. And the proposed algorithm outperforms K-MEANS in terms of detecting accuracy, robustness and edge's distinction. This paper demonstrates the new method is meaningful to camouflaged targets detecting.
Hua, Wenshen; Liu, Xun; Yang, Jia
According to the imaging principle and characteristic of LASIS (Large Aperture Static Interference Imaging Spectrometer), we discovered that the 3D (three dimensional) image sequences formed by different interference pattern frames, which were formed in the imaging process of LASIS Interference hyperspectral image, had much stronger correlation than the original interference hyperspectral image sequences, either in 2D (two dimensional) spatial domain or in the spectral domain. We put this characteristic into image compression and proposed an adaptive OPD (optical path difference) and dislocation prediction algorithm for interference hyperspectral image compression. Compared the new algorithm proposed in this paper with Dual-Direction Prediction  proposed in 2009, lots of experimental results showed that the prediction error entropy of the new algorithm was much smaller. In the prediction step of lifting wavelet transform, this characteristic would also reduce the entropy of coefficients in high frequency significantly, which would be more advantageous for quantification coding .
Wen, Jia; Ma, Caiwen; Shui, Penglang
Mapping tools are needed to document the location and extent of Phragmites australis, a tall grass that invades coastal marshes throughout North America, displacing native plant species and degrading wetland habitat. Mapping Phragmites is particularly challenging in the freshwater Great Lakes coastal wetlands due to dynamic lake levels and vegetation diversity. We tested the applicability of Hyperion hyperspectral satellite imagery for mapping Phragmites in wetlands of the west coast of Green Bay in Wisconsin, U.S.A. A reference spectrum created using Hyperion data from several pure Phragmites stands within the image was used with a Spectral Correlation Mapper (SCM) algorithm to create a raster map with values ranging from 0 to 1, where 0 represented the greatest similarity between the reference spectrum and the image spectrum and 1 the least similarity. The final two-class thematic classification predicted monodominant Phragmites covering 3.4% of the study area. Most of this was concentrated in long linear features parallel to the Green Bay shoreline, particularly in areas that had been under water only six years earlier when lake levels were 66??cm higher. An error matrix using spring 2005 field validation points (n = 129) showed good overall accuracy-81.4%. The small size and linear arrangement of Phragmites stands was less than optimal relative to the sensor resolution, and Hyperion's 30??m resolution captured few if any pure pixels. Contemporary Phragmites maps prepared with Hyperion imagery would provide wetland managers with a tool that they currently lack, which could aid attempts to stem the spread of this invasive species. ?? 2006 Elsevier Inc. All rights reserved.
Pengra, B.W.; Johnston, C.A.; Loveland, T.R.
acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non rights reserved. 1. Introduction Inland, estuarine, and coastal waters represent complex and highly using spaceborne sensors in monitoring inland, estuarine,
Fusarium head blight is a fungal disease that affects the world's small grains, such as wheat and barley. Attacking the spikelets during development, the fungus causes a reduction of yield and grain of poorer processing quality. It also is a health concern because of the secondary metabolite, deoxynivalenol, which often accompanies the fungus. While chemical methods exist to measure the concentration of the mycotoxin and manual visual inspection is used to ascertain the level of Fusarium damage, research has been active in developing fast, optically based techniques that can assess this form of damage. In the current study a near-infrared (1000-1700 nm) hyperspectral image system was assembled and applied to Fusarium-damaged kernel recognition. With anticipation of an eventual multispectral imaging system design, 5 wavelengths were manually selected from a pool of 146 images as the most promising, such that when combined in pairs or triplets, Fusarium damage could be identified. We present the results of two pairs of wavelengths [(1199, 1474 nm) and (1315, 1474 nm)] whose reflectance values produced adequate separation of kernels of healthy appearance (i.e., asymptomatic condition) from kernels possessing Fusarium damage.
Delwiche, Stephen R.; Kim, Moon S.; Dong, Yanhong
An imaging Fourier-transform spectrometer has been used to determine low spatial resolution temperature and chemical species concentration distributions of aircraft jet engine exhaust plumes. An overview of the imaging Fourier transform spectrometer and the methodology of the project is presented. Results to date are shared and future work is discussed. Exhaust plume data from a Turbine Technologies, LTD, SR-30 turbojet engine at three engine settings was collected using a Telops Field-portable Imaging Radiometric Spectrometer Technology Mid-Wave Extended (FIRST-MWE). Although the plume exhibited high temporal frequency fluctuations, temporal averaging of hyper-spectral data-cubes produced steady-state distributions, which, when co-added and Fourier transformed, produced workable spectra. These spectra were then reduced using a simplified gaseous effluent model to fit forward-modeled spectra obtained from the Line-By-Line Radiative Transfer Model (LBLRTM) and the high-resolution transmission (HITRAN) molecular absorption database to determine approximate temperature and concentration distributions. It is theorized that further development of the physical model will produce better agreement between measured and modeled data.
Bowen, Spencer; Bradley, Kenneth; Gross, Kevin; Perram, Glen; Marciniak, Michael
Classification and spectral unmixing are two very important tasks for hyperspectral data exploitation. Although many studies exist in both areas, the combined use of both approaches has not been widely explored in the literature. Since hyperspectral images are generally dominated by mixed pixels, spectral unmixing can particularly provide a useful source of information for classification purposes. In previous work, we have demonstrated that spectral unmixing can be used as an effective approach for feature extraction prior to supervised classification of hyperspectral data using support vector machines (SVMs). Unmixing-based features do not dramatically improve classification accuracies with regards to features provided by classic techniques such as the minimum noise fraction (MNF), but they can provide a better characterization of small classes. Also, these features are potentially easier to interpret due to their physical meaning (in spectral unmixing, the features represent the abundances of real materials present in the scene). In this paper, we develop a new strategy for feature extraction prior to supervised classification of hyperspectral images. The proposed method first performs unsupervised multidimensional clustering on the original hyperspectral image to implicitly include spatial information in the process. The cluster centres are then used as representative spectral signatures for a subsequent (partial) unmixing process, and the resulting features are used as inputs to a standard (supervised) classification process. The proposed strategy is compared to other classic and unmixing feature extraction methods presented in the literature. Our experiments, conducted with several reference hyperspectral images widely used for classification purposes, reveal the effectiveness of the proposed approach.
Dópido, Inmaculada; Villa, Alberto; Plaza, Antonio
Hyperspectral sensors are electro-optic sensors which typically operate in visible and near infrared bands. Their characteristic property is the ability to resolve a relatively large number (i.e., tens to hundreds) of contiguous spectral bands to produce a detailed profile of the electromagnetic spectrum. In contrast, multispectral sensors measure relatively few non-contiguous spectral bands. Like multispectral sensors, hyperspectral sensors are often also imaging sensors, measuring spectra over an array of spatial resolution cells. The data produced may thus be viewed as a three dimensional array of samples in which two dimensions correspond to spatial position and the third to wavelength. Because they multiply the already large storage/transmission bandwidth requirements of conventional digital images, hyperspectral sensors generate formidable torrents of data. Their fine spectral resolution typically results in high redundancy in the spectral dimension, so that hyperspectral data sets are excellent candidates for compression. Although there have been a number of studies of compression algorithms for multispectral data, we are not aware of any published results for hyperspectral data. Three algorithms for hyperspectral data compression are compared. They were selected as representatives of three major approaches for extending conventional lossy image compression techniques to hyperspectral data. The simplest approach treats the data as an ensemble of images and compresses each image independently, ignoring the correlation between spectral bands. The second approach transforms the data to decorrelate the spectral bands, and then compresses the transformed data as a set of independent images. The third approach directly generalizes two-dimensional transform coding by applying a three-dimensional transform as part of the usual transform-quantize-entropy code procedure. The algorithms studied all use the discrete wavelet transform. In the first two cases, a wavelet transform coder was used for the two-dimensional compression. The third case used a three dimensional extension of this same algorithm.
Evans, Bruce; Ringer, Brian; Yeates, Mathew
Hyperspectral remote sensing produces large volumes of data, quite often requiring hundreds of megabytes to gigabytes of memory storage for a small geographical area for onetime data collection. Although the high spectral resolution of hyperspectral data is quite useful for capturing and discriminating subtle differences in geospatial characteristics of the target, it contains redundant information at the band level. The
S. G. Bajwa; P. Bajcsy; P. Groves; L. F. Tian
Detection and identification of bacteria are important for health and safety. Hyperspectral imaging offers the potential to capture unique spectral patterns and spatial information from bacteria which can then be used to detect and differentiate bacterial species. Here, hyperspectral imaging has been used to characterize different bacterial colonies and investigate their growth over time. Six bacterial species (Pseudomonas fluorescens, Escherichia coli, Serratia marcescens, Salmonella enterica, Staphylococcus aureus, Enterobacter aerogenes) were grown on tryptic soy agar plates. Hyperspectral data were acquired immediately after, 24 hours after, and 96 hours after incubation. Spectral signatures from bacterial colonies demonstrated repeatable measurements for five out of six species. Spatial variations as well as changes in spectral signatures were observed across temporal measurements within and among species at multiple wavelengths due to strengthening or weakening reflectance signals from growing bacterial colonies based on their pigmentation. Between-class differences and within-class similarities were the most prominent in hyperspectral data collected 96 hours after incubation.
Mehrübeoglu, Mehrube; Buck, Gregory W.; Livingston, Daniel W.
Abstract-- We present an algorithm for lossy compression of hyperspectral images generated. The compression of hyperspectral images was investigated in  and . In , the information content, and the residual (difference image) is compressed using the Set Partitioning in Hierarchical Trees (SPIHT
We report the implementation of an image sensor chip, termed wavefront image sensor chip (WIS), that can measure both intensity/amplitude and phase front variations of a light wave separately and quantitatively. By monitoring the tightly confined transmitted light spots through a circular aperture grid in a high Fresnel number regime, we can measure both intensity and phase front variations with a high sampling density (11 µm) and high sensitivity (the sensitivity of normalized phase gradient measurement is 0.1 mrad under the typical working condition). By using WIS in a standard microscope, we can collect both bright-field (transmitted light intensity) and normalized phase gradient images. Our experiments further demonstrate that the normalized phase gradient images of polystyrene microspheres, unstained and stained starfish embryos, and strongly birefringent potato starch granules are improved versions of their corresponding differential interference contrast (DIC) microscope images in that they are artifact-free and quantitative. Besides phase microscopy, WIS can benefit machine recognition, object ranging, and texture assessment for a variety of applications. PMID:20721059
Cui, Xiquan; Ren, Jian; Tearney, Guillermo J.; Yang, Changhuei
We report the implementation of an image sensor chip, termed wavefront image sensor chip (WIS), that can measure both intensity/amplitude and phase front variations of a light wave separately and quantitatively. By monitoring the tightly confined transmitted light spots through a circular aperture grid in a high Fresnel number regime, we can measure both intensity and phase front variations with a high sampling density (11 microm) and high sensitivity (the sensitivity of normalized phase gradient measurement is 0.1 mrad under the typical working condition). By using WIS in a standard microscope, we can collect both bright-field (transmitted light intensity) and normalized phase gradient images. Our experiments further demonstrate that the normalized phase gradient images of polystyrene microspheres, unstained and stained starfish embryos, and strongly birefringent potato starch granules are improved versions of their corresponding differential interference contrast (DIC) microscope images in that they are artifact-free and quantitative. Besides phase microscopy, WIS can benefit machine recognition, object ranging, and texture assessment for a variety of applications. PMID:20721059
Cui, Xiquan; Ren, Jian; Tearney, Guillermo J; Yang, Changhuei
Spectroscopic imaging has been an increasingly critical approach for unveiling specific molecules in biological environments. Towards this goal, we demonstrate hyperspectral stimulated Raman loss (SRL) imaging by intra-pulse spectral scanning through a femtosecond pulse shaper. The hyperspectral stack of SRL images is further analyzed by a multivariate curve resolution (MCR) method to reconstruct quantitative concentration images for each individual component and retrieve the corresponding vibrational Raman spectra. Using these methods, we demonstrate quantitative mapping of dimethyl sulfoxide concentration in aqueous solutions and in fat tissue. Moreover, MCR is performed on SRL images of breast cancer cells to generate maps of principal chemical components along with their respective vibrational spectra. These results show the great capability and potential of hyperspectral SRL microscopy for quantitative imaging of complicated biomolecule mixtures through resolving overlapped Raman bands. PMID:23198914
Zhang, Delong; Wang, Ping; Slipchenko, Mikhail N.; Ben-Amotz, Dor; Weiner, Andrew M.; Cheng, Ji-Xin
Proposed subsystem of small auxiliary imaging sensors yields additional data on motion of linear imaging sensor scanned across scene perpendicular to its length. Sensors yield data on components of motion to which inertial, radar, and air-speed detectors insensitive. Additional data on motion enhances geometric fidelity.
Jones, Kenneth L.
A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the mean squared error (MSE) performance of an eight-state ECPTCQ system exceeds that of entropy-constrained differential pulse code modulation (ECDPCM) by up to 1.0 dB. In addition, a hyperspectral image compression system is developed, which utilizes ECPTCQ. A hyperspectral
Glen P. Abousleman; Michael W. Marcellin; Bobby R. Hunt
Stand-off identification in the field using thermal infrared spectrometers (hyperspectral) is a maturing technique for gases and aerosols. However, capabilities to identify solid-phase materials on the surface lag substantially, particularly for identification in the field without benefit of ground truth (e.g. for "denied areas"). Spectral signatures of solid phase materials vary in complex and non-intuitive ways, including non-linear variations with surface texture, particle size, and intimate mixing. Also, in contrast to airborne or satellite measurements, reflected downwelling radiance strongly affects the signature measured by field spectrometers. These complex issues can confound interpretations or cause a misidentification in the field. Problems that remain particularly obstinate are (1) low ambiguity identification when there is no accompanying ground truth (e.g. measurements of denied areas, or Mars surface by the 2003 Mars lander spectrometer); (2) real- or near real-time identification, especially when a low ambiguity answer is critical; (3) identification of intimate mixtures (e.g. two fine powders mixed together) and targets composed of very small particles (e.g. aerosol fallout dust, some tailings); and (4) identification of non-diffuse targets (e.g. smooth coatings such as paint and desert varnish), particularly when measured at a high emission angle. In most studies that focus on gas phase targets or specific manmade targets, the solid phase background signatures are called "clutter" and are thrown out. Here we discuss our field spectrometer images measured of test targets that were selected to include a range of particle sizes, diffuse, non-diffuse, high, and low reflectance materials. This study was designed to identify and improve understanding of the issues that complicate stand-off identification in the field, with a focus on developing identification capabilities to proceed without benefit of ground truth. This information allows both improved measurement protocols and identification quality. The Aerospace Corporation has a mature program for field hyperspectral measurements using van-mounted thermal-infrared spectrometers that raster-scan images. Aerospace is a non-profit Federally Funded Research and Development Center (FFRDC), managed by the Department of Defense. The precisely controlled viewing geometery, imaging capabilities, and sensitivity of the spectrometers used are critical to identifying and studying issues that can confound interpretations or cause a misidentification. We have released a portion of this data set publicly, and encourage researchers interested in the data set to contact us. More information is at www.lpi.usra.edu/science/kirkland.
Kirkland, Laurel E.; Herr, Kenneth C.; Adams, Paul M.; McAfee, John; Salisbury, John
Hyperspectral coherent anti-Stokes Raman scattering (CARS) microscopy has provided an imaging tool for extraction of 3-dimensional volumetric information, as well as chemically-sensitive spectral information. These techniques have been used in a variety of different domains including biophysics, geology, and material science. The measured CARS spectrum results from interference between the Raman response of the sample and a non-resonant background. We have collected four dimensional data sets (three spatial dimensions, plus spectra) and extracted Raman response from the CARS spectrum using a Kramers-Kronig transformation. However, the three dimensional images formed by a CARS microscope are distorted by interference, some of which arises because of the Gouy phase shift. This type of interference comes from the axial position of the Raman resonant object in the laser focus. We studied how the Gouy phase manifests itself in the spectral domain by investigating microscopic diamonds and nitrobenzene droplets in a CARS microscope. Through experimental results and numerical calculation using finite-diference time-domain (FDTD) methods, we were able to demonstrate the relationship between the spatial configuration of the sample and the CARS spectral response in three dimensional space.
Barlow, Aaron M.; Popov, Konstantin; Andreana, Marco; Moffatt, Douglas J.; Ridsdale, Andrew; Slepkov, Aaron D.; Ramunno, Lora; Stolow, Albert
HyperSpectral Imaging (HSI) is based on the utilization of an integrated hardware and software (HW&SW) platform embedding conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool, for non-destructive analysis, in many research and industrial sectors. The possibility to apply on-line HSI based techniques in order to identify and quantify specific particulate solid systems characteristics is presented and critically evaluated. The originally developed HSI based logics can be profitably applied in order to develop fast, reliable and lowcost strategies for: i) quality control of particulate products that must comply with specific chemical, physical and biological constraints, ii) performance evaluation of manufacturing strategies related to processing chains and/or realtime tuning of operative variables and iii) classification-sorting actions addressed to recognize and separate different particulate solid products. Case studies, related to recent advances in the application of HSI to different industrial sectors, as agriculture, food, pharmaceuticals, solid waste handling and recycling, etc. and addressed to specific goals as contaminant detection, defect identification, constituent analysis and quality evaluation are described, according to authors' originally developed application.
Bonifazi, Giuseppe; Serranti, Silvia
Scab (Fusarium head blight) is a disease that causes wheat kernels to be shriveled, underweight, and difficult to mill. Scab is also a health concern because of the possible concomitant production of the mycotoxin deoxynivalenol. Current official inspection procedures entail manual human inspection. A study was undertaken to explore the possibility of detecting scab-damaged wheat kernels by machine vision. A custom-made hyperspectral imaging system, possessing a wavelength range of 425 to 860 nm with neighboring bands 3.7 nm apart, a spatial resolution of 0.022 mm2/pixel, and 16-bit per pixel dynamic range, gathered images of non-touching kernels from three wheat varieties. Each variety was represented by 32 normal and 32 scab-damaged kernels. From a search of wavelengths that could be used to separate the two classes (normal vs. scab), a linear discriminant function was constructed from the best R((lambda) 1)/R((lambda) 2), based on the assumption of a multivariate normal distribution for each class and the pooling of the covariance error that averaged between 2 and 17%, dependent on wheat variety. With expansion to the testing of more varieties, a two-to-four wavelength machine vision system appears to be a feasible alternative to manual inspection.
Delwiche, Stephen R.; Kim, Moon S.
Hyperspectral images in the near infrared range (HSI-NIR) were evaluated as a nondestructive method to detect fraud in documents. Three different types of typical forgeries were simulated by (a) obliterating text, (b) adding text and (c) approaching the crossing lines problem. The simulated samples were imaged in the range of 928-2524 nm with spectral and spatial resolutions of 6.3 nm and 10 ?m, respectively. After data pre-processing, different chemometric techniques were evaluated for each type of forgery. Principal component analysis (PCA) was performed to elucidate the first two types of adulteration, (a) and (b). Moreover, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used in an attempt to improve the results of the type (a) obliteration and type (b) adding text problems. Finally, MCR-ALS and Partial Least Squares-Discriminant Analysis (PLS-DA), employed as a variable selection tool, were used to study the type (c) forgeries, i.e. crossing lines problem. Type (a) forgeries (obliterating text) were successfully identified in 43% of the samples using both the chemometric methods (PCA and MCR-ALS). Type (b) forgeries (adding text) were successfully identified in 82% of the samples using both the methods (PCA and MCR-ALS). Finally, type (c) forgeries (crossing lines) were successfully identified in 85% of the samples. The results demonstrate the potential of HSI-NIR associated with chemometric tools to support document forgery identification. PMID:25118338
Silva, Carolina S; Pimentel, Maria Fernanda; Honorato, Ricardo S; Pasquini, Celio; Prats-Montalbán, José M; Ferrer, Alberto
In order to guarantee the observed data with high spatial and wavelength resolution of hyperspectral/multispectral imagers, it is necessary to evaluate the difference of the spectral sensitivity among the detector devices arrayed twodimensionally and correct spectral and spatial misregistrations and the effect of stray light. However, there are tens of thousands of detectors in hyperspectral imagers, so they have to be evaluated in parallel by the special technique. Therefore, a light-source system which has high radiance with the spatial uniformity and widely tunable wavelengthrange is required instead of the conventional lamp system. In this presentation, we report the new setup of the supercontinuum(SC)-source-monochromator system and its fundamental performance. The SC source covers a wavelength range of 450-2400 nm, and its total output power is up to 6 W. We effectively coupled a high-power SC laser to a single monochromator and obtained spatial uniformity through an integrating sphere or a relay-optics system. The radiance three or more magnitudes higher than a tungsten halogen lamp was measured with the supercontinuum-source based system. The stability of output power and the spatial uniformity of radiance at the integrating-sphere port were also evaluated. Using the system, spectral misregistrations and responsivities of a hyperspectral imager, which is consist of a polychromator and two-dimensional array of CCD, were measured.
Yamaguchi, Yu; Yamada, Yoshiro; Ishii, Juntoro
Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. Technologies that can efficiently\\u000a identify citrus canker would assure fruit quality and safety and enhance the competitiveness and profitability of the citrus\\u000a industry. This research was aimed to investigate the potential of using hyperspectral imaging technique for detecting canker\\u000a lesions on citrus fruit. A portable
Jianwei Qin; Thomas F. Burks; Moon S. Kim; Kuanglin Chao; Mark A. Ritenour
Abstract Food quality and safety is the foremost issue for consumers, retailers as well as regulatory authorities. Most quality parameters are assessed by traditional methods, which are time consuming, laborious and associated with inconsistency and variability. Non-destructive methods have been developed to objectively measure quality attributes for various kinds of food. In recent years, hyperspectral imaging (HSI) has matured into one of the most powerful tools for quality evaluation of agricultural and food products. Hyperspectral imaging allows characterization of a sample's chemical composition (spectroscopic component) and external features (imaging component) in each point of the image with full spectral information. In order to track the latest research developments of this technology, this paper gives a detailed overview of the theory and fundamentals behind this technology and discusses its applications in the field of quality evaluation of agricultural products. Additionally, future potentials of hyperspectral imaging are also reported. PMID:24915395
Liu, Dan; Zeng, Xin-An; Sun, Da-Wen
is an important application of image analysis. Many practical applications, such as precision agricultureSupervised super resolution to improve the resolution of hyperspectral images classification maps & Image Dept., Grenoble Institute of Technology - INP, France 961 rue de la Houille Blanche, 38402
Coherent Raman microspectroscopy imaging is an emerging technique for noninvasive, chemically specific optical imaging, which can be potentially used to analyze the chemical composition and its distribution in biological tissues. In this report, a hierarchical cluster analysis was applied to hyperspectral coherent anti-Stokes Raman imaging of different chemical species through a turbid medium. It was demonstrated that by using readily
Rajan Arora; Georgi I. Petrov; Vladislav V. Yakovlev
The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000-1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments. PMID:25310472
Lee, Hoonsoo; Kim, Moon S; Jeong, Danhee; Delwiche, Stephen R; Chao, Kuanglin; Cho, Byoung-Kwan
The Wide-field Imaging Interferometer testbed (WIIT) at NASA's Goddard Space Flight Center uses a dual-Michelson interferometric technique. The WIIT combines stellar interferometry with Fourier-transform interferometry to produce high-resolution spatial-spectral data over a large field-of-view. This combined technique could be employed on future NASA missions such as the Space Infrared Interferometric Telescope (SPIRIT) and the Sub-millimeter Probe of the Evolution of Cosmic Structure (SPECS). While both SPIRIT and SPECS would operate at far-infrared wavelengths, the WIIT demonstrates the dual-interferometry technique at visible wavelengths. The WIIT will produce hyperspectral image data, so a true hyperspectral object is necessary. A calibrated hyperspectral image projector (CHIP) has been constructed to provide such an object. The CHIP uses Digital Light Processing (DLP) technology to produce customized, spectrally-diverse scenes. CHIP scenes will have approximately 1.6-micron spatial resolution and the capability of . producing arbitrary spectra in the band between 380 nm and 1.6 microns, with approximately 5-nm spectral resolution. Each pixel in the scene can take on a unique spectrum. Spectral calibration is achieved with an onboard fiber-coupled spectrometer. In this paper we describe the operation of the CHIP. Results from the WIIT observations of CHIP scenes will also be presented.
Bolcar, Matthew R.; Leisawitz, David; Maher, Steve; Rinehart, Stephen
The NASA image-based geological expert system was applied to analyze remotely sensed hyperspectral image data. The major objective is for geologists to identify the earth surface mineral properties directly from the airborne and spaceborne imaging spectrometer data. With certain constraints, it is shown that the system can identify correctly different classes of mineral. It has the built-in learning paradigm to enhance the confidence factor of mineral identification. A very powerful natural language system was incorporated as the user-friendly front end, and the concurrent processing efficiency of the frame-based knowledge representation in the hypercube microsupercomputer simulation was tested.
Chiou, W. C., Sr.
While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.
Haaland, David Michael; Stork, Christopher Lyle; Keenan, Michael Robert
Hyperspectral image compression is an important task in remotely sensed Earth Observation as the dimensionality of this kind of image data is ever increasing. This requires on-board compression in order to optimize the donwlink connection when sending the data to Earth. A successful algorithm to perform lossy compression of remotely sensed hyperspectral data is the iterative error analysis (IEA) algorithm, which applies an iterative process which allows controlling the amount of information loss and compression ratio depending on the number of iterations. This algorithm, which is based on spectral unmixing concepts, can be computationally expensive for hyperspectral images with high dimensionality. In this paper, we develop a new parallel implementation of the IEA algorithm for hyperspectral image compression on graphics processing units (GPUs). The proposed implementation is tested on several different GPUs from NVidia, and is shown to exhibit real-time performance in the analysis of an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) data sets collected over different locations. The proposed algorithm and its parallel GPU implementation represent a significant advance towards real-time onboard (lossy) compression of hyperspectral data where the quality of the compression can be also adjusted in real-time.
Sánchez, Sergio; Plaza, Antonio
As an emerging technology, hyperspectral imaging (HSI) combines both the chemical specificity of spectroscopy and the spatial resolution of imaging, which may provide a non-invasive tool for cancer detection and diagnosis. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. In this paper, we introduce a tensor-based computation and modeling framework for the analysis of hyperspectral images to detect head and neck cancer. The proposed classification method can distinguish between malignant tissue and healthy tissue with an average sensitivity of 96.97% and an average specificity of 91.42% in tumor-bearing mice. The hyperspectral imaging and classification technology has been demonstrated in animal models and can have many potential applications in cancer research and management. PMID:25328639
Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei
The Information-efficient Spectral Imaging Sensor (ISIS) approach to spectral imaging seeks to bridge the gap between tuned multispectral and fixed hyperspectral imaging sensors. By allowing the definition of completely general spectral filter functions, truly optimal measurements can be made for a given task. These optimal measurements significantly improve signal-to-noise ratio (SNR) and speed, minimize data volume and data rate, while preserving classification accuracy. The following paper investigates the application of the ISIS sensing approach in two sample biomedical applications: prostate and colon cancer screening. It is shown that in these applications, two to three optimal measurements are sufficient to capture the majority of classification information for critical sample constituents. In the prostate cancer example, the optimal measurements allow 8% relative improvement in classification accuracy of critical cell constituents over a red, green, blue (RGB) sensor. In the colon cancer example, use of optimal measurements boost the classification accuracy of critical cell constituents by 28% relative to the RGB sensor. In both cases, optimal measurements match the performance achieved by the entire hyperspectral data set. The paper concludes that an ISIS style spectral imager can acquire these optimal spectral images directly, allowing improved classification accuracy over an RGB sensor. Compared to a hyperspectral sensor, the ISIS approach can achieve similar classification accuracy using a significantly lower number of spectral samples, thus minimizing overall sample classification time and cost.
Gentry, S.M.; Levenson, R.
A hyperspectral image (HSI) is always modeled as a three-dimensional tensor, with the first two dimensions indicating the spatial domain and the third dimension indicating the spectral domain. The classical matrix-based denoising methods require to rearrange the tensor into a matrix, then filter noise in the column space, and finally rebuild the tensor. To avoid the rearranging and rebuilding steps, the tensor-based denoising methods can be used to process the HSI directly by employing multilinear algebra. This paper presents a survey on three newly proposed HSI denoising methods and shows their performances in reducing noise. The first method is the Multiway Wiener Filter (MWF), which is an extension of the Wiener filter to data tensors, based on the TUCKER3 decomposition. The second one is the PARAFAC filter, which removes noise by truncating the lower rank K of the PARAFAC decomposition. And the third one is the combination of multidimensional wavelet packet transform (MWPT) and MWF (MWPT-MWF), which models each coefficient set as a tensor and then filters each tensor by applying MWF. MWPT-MWF has been proposed to preserve rare signals in the denoising process, which cannot be preserved well by using the MWF or PARAFAC filters. A real-world HYDICE HSI data is used in the experiments to assess these three tensor-based denoising methods, and the performances of each method are analyzed in two aspects: signal-to-noise ratio and improvement of subsequent target detection results.