MASTER - A New Instrument for Hyperspectral Analysis from the Visible to Thermal Infrared
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Hyperspectral Analysis Thermal Infrared↗
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Results for “hyperspectral analysis”
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MASTER - A New Instrument for Hyperspectral Analysis From the Visible to Thermal Infrared
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MODIS/ASTER MASTER ASTER MODIS Thermal Infrared Hy↗
NASA image-based geological expert system development project for hyperspectral image analysis
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.↗
Sparse Superpixel Unmixing for Hyperspectral Image Analysis
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↗
Sparse Superpixel Unmixing for Exploratory Analysis of CRISM Hyperspectral Images
Fast automated analysis of hyperspectral imagery can inform observation planning and tactical decisions during planetary exploration. Products such as mineralogical maps can focus analysts' attention on areas of interest and assist data mining in large hyperspectral catalogs. In this work, sparse spectral unmixing drafts mineral abundance maps with Compact Reconnaissance Imaging Spectrometer (CRISM) images from the Mars Reconnaissance Orbiter. We demonstrate a novel "superpixel" segmentation strategy enabling efficient unmixing in an interactive session. Tests correlate automatic unmixing results based on redundant spectral libraries against hand-tuned summary products currently in use by CRISM researchers.
Image Segmentation↗
Hyperspectral data analysis procedures with reduced sensitivity to noise
Multispectral sensor systems have become steadily improved over the years in their ability to deliver increased spectral detail. With the advent of hyperspectral sensors, including imaging spectrometers, this technology is in the process of taking a large leap forward, thus providing the possibility of enabling delivery of much more detailed information. However, this direction of development has drawn even more attention to the matter of noise and other deleterious effects in the data, because reducing the fundamental limitations of spectral detail on information collection raises the limitations presented by noise to even greater importance. Much current effort in remote sensing research is thus being devoted to adjusting the data to mitigate the effects of noise and other deleterious effects. A parallel approach to the problem is to look for analysis approaches and procedures which have reduced sensitivity to such effects. We discuss some of the fundamental principles which define analysis algorithm characteristics providing such reduced sensitivity. One such analysis procedure including an example analysis of a data set is described, illustrating this effect.
Landgrebe, David A.↗
Hyperspectral Image Analysis Program
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.
Source record↗
Hyperspectral data analysis for estimation of foliar biochemical content along the Oregon transect
The NASA Oregon Transect Ecosystem Research (OTTER) project completed a data acquisition phase. Data were acquired with several airborne imaging spectrometers. Included were the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) aboard the ER-2, the Advanced Solidstate Array Spectrometer (ASAS) aboard the C-130, and the Fluorescence Line Imager (FLI) and Compact Airborne Spectrographic Imager (CASI), both aboard light aircraft. In addition, Spectron visible and near-infrared data were acquired in transects across study areas from a low-altitude ultralight craft. Sunphotometer data were taken approximately coincident with each overflight for atmospheric correction of the aircraft data.
Johnson, Lee F.↗
On the use of stochastic process-based methods for the analysis of hyperspectral data
Further development in remote sensing technology requires refinement of information system design aspects, i.e., the ability to specify precisely the data to collect and the means to extract increasing amounts of information from the increasingly rich and complex data stream created. One of the principal directions of advance is that data from much larger numbers of spectral bands can be collected, but with significantly increased signal-to-noise ratio. The theory of stochastic or random processes may be applied to the modeling of second-order variations. A multispectral data set with a large number of spectral bands is analyzed using standard pattern recognition techniques. The data were classified using first a single spectral feature, then two, and continuing on with greater and greater numbers of features. Three different classification schemes are used: a standard maximum likelihood Gaussian scheme; the same approach with the mean values of all classes adjusted to be the same; and the use of a minimum distance to means scheme such that mean differences are used.
Landgrebe, David A.↗
Neural Network for Analysis of Hyperspectral Imagery
Each material has unique spectral distribution that can be viewed as its fingerprint. Advanced hyper and ultra spectral sensors will produce large amount of data. Therefore, advanced capabilities are required that can effectively deal with large data cubes, possibly in real time.
hyperspectral imagery neural network↗
New effective spectral matching measures for hyperspectral data analysis
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Chandan Kumar↗
Superpixel-Based Active Learning and Online Feature Importance Learning for Hyperspectral Image Analysis
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Jielian Guo↗
Optical Performance of Reflectivity Control Devices for Solar Sail Applications
Reflectivity control devices (RCDs) based on polymer dispersed liquid crystals were fabricated for Solar Cruiser, a SmallSat NASA Pathfinder Mission consisting of a 1653 square meter solar sail that would establish an artificial orbit sunward of the L1 Lagrange point for heliophysics observations. Here we describe the optical characterization of these birefringent electro-optic devices including thin film measurements and analysis, hyperspectral bidirectional reflectance distribution function measurements, and radiometric analysis. These measurements demonstrate the promise of RCDs for roll control and momentum management of solar sails.
Radiometry↗
Optical Performance of Reflectivity Control Devices (RCDs) for Solar Sail Applications
Reflectivity control devices (RCDs) based on polymer dispersed liquid crystals were fabricated for Solar Cruiser, a SmallSat NASA Pathfinder Mission consisting of a 1653 square meter solar sail that would establish an artificial orbit sunward of the L1 Lagrange point for heliophysics observations. Here we describe the optical characterization of these birefringent electro-optic devices including thin film measurements and analysis, hyperspectral bidirectional reflectance distribution function measurements, and radiometric analysis. These measurements demonstrate the promise of RCDs for roll control and momentum management of solar sails.
Solar Sails↗
Analysis of AOTF Hyperspectral Imagery
This paper reports results from an analysis of polarimetric hyperspectral imagery collected using a prototype acousto-optic tunable filter (AOTF) instrument in an outdoor environment. Spectra, derivative spectra, and polarization spectra in the image cube form were studied. The issue concerns spectral and polarization signatures of vegetation, contributions due to aerosol, and man-made object detection. The result illustrates potentials of the technology for a variety of remote sensing applications.
acousto-optic↗
Superpixel-Augmented Endmember Detection for Hyperspectral Images
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.↗
Dynamic descriptors for contextual classification of remotely sensed hyperspectral image data analysis
The extended CIE transformation procedure of Chiou (1984) is applied to five sets of remotely sensed 0.4-2.5-micron spectrometric data on field crops including winter and spring wheat, corn, and soybeans) obtained at the Purdue University Laboratory for Applications of - Remote Sensing during 1977-1980 using the techniques described by Hinzman (1981). The results are presented in tables and chromaticity diagrams and it is found that each crop has an identifiable time-variant spectral characteristic permitting determination of seasonal growth patterns. The applicability of the method to data from airborne and spaceborne imaging spectrometers is indicated.
Chiou, W. C., Sr.↗
Derivative Analysis of AVIRIS Hyperspectral Data for the Detection of Plant Stress
A remote sensing campaign was conducted over a U.S. Department of Agriculture test site at Shelton, Nebraska. The test field was set off in blocks that were differentially treated with nitrogen. Four replicates of 0-kg/ha to 200-kg/ha, in 50-kg/ha increments, were present. Low-altitude AVIRIS hyperspectral data were collected over the site in 224 spectral bands. Simultaneously, ground data were collected to support the airborne imagery. In an effort to evaluate published, derivative-based algorithms for the detection of plant stress, different derivative-based approaches were applied to the collected AVIRIS image cube. The results indicate that, given good quality hyperspectral imagery, derivative techniques compare favorably with simple, well known band ratio algorithms for detection of plant stress.
Estep, Lee↗