Analysis Of Imaging Spectrometer Data Research Articles

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques.

WASI-2D: A software tool for regionally optimized analysis of imaging spectrometer data from deep and shallow waters

An image processing software has been developed which allows quantitative analysis of multi- and hyperspectral data from oceanic, coastal and inland waters. It has been implemented into the Water Colour Simulator WASI, which is a tool for the simulation and analysis of optical properties and light field parameters of deep and shallow waters. The new module WASI-2D can import atmospherically corrected images from airborne sensors and satellite instruments in various data formats and units like remote sensing reflectance or radiance. It can be easily adapted by the user to different sensors and to optical properties of the studied area. Data analysis is done by inverse modelling using established analytical models. The bio-optical model of the water column accounts for gelbstoff (coloured dissolved organic matter, CDOM), detritus, and mixtures of up to 6 phytoplankton classes and 2 spectrally different types of suspended matter. The reflectance of the sea floor is treated as sum of up to 6 substrate types. An analytic model of downwelling irradiance allows wavelength dependent modelling of sun glint and sky glint at the water surface. The provided database covers the spectral range from 350 to 1000nm in 1nm intervals. It can be exchanged easily to represent the optical properties of water constituents, bottom types and the atmosphere of the studied area.

Analysis of Imaging Spectrometer Data Using $N$-Dimensional Geometry and a Mixture-Tuned Matched Filtering Approach

Imaging spectrometers collect unique data sets that are simultaneously a stack of spectral images and a spectrum for each image pixel. While these data can be analyzed using approaches designed for multispectral images, or alternatively by looking at individual spectra, neither of these takes full advantage of the dimensionality of the data. Imaging spectrometer spectral radiance data or derived apparent surface reflectance data can be cast as a scattering of points in an n-dimensional Euclidean space, where n is the number of spectral channels and all axes of the n-space are mutually orthogonal. Every pixel in the data set then has a point associated with it in the n- d space, with its Cartesian coordinates defined by the values in each spectral channel. Given n-dimensional data, convex and affine geometry concepts can be used to identify the purest pixels in a given scene (the “endmembers”). N-dimensional visualization techniques permit human interpretation of all spectral information of all image pixels simultaneously and projection of the endmembers back to their locations in the imagery and to their spectral signatures. Once specific spectral endmembers are defined, partial linear unmixing (mixture-tuned matched filtering or “MTMF”) can be used to spectrally unmix the data and to accurately map the apparent abundance of a known target material in the presence of a composite background. MTMF incorporates the best attributes of matched filtering but extends that technique using the linear mixed-pixel model, thus leading to high selectivity between similar materials and minimizing classification and mapping errors for analysis of imaging spectrometer data.

Preprocessing of EO-1 Hyperion data

A procedure for processing hyperspectral data acquired with Hyperion has been developed with an aim to correct for sensor artifacts and atmospheric and geometric effects. Advances in preprocessing of hyperspectral remote sensing data have enabled more accurate atmospheric correction and have led to the development of new information extraction techniques in the areas of agriculture, forestry, geosciences, and environmental monitoring. These processing and analysis tools have been incorporated into Imaging Spectrometer Data Analysis Systems (ISDAS), a software package developed at the Canada Centre for Remote Sensing (CCRS). The procedure, as applied for Hyperion data, begins with geometric corrections to the short-wave infrared (SWIR) component to register the SWIR and visible near-infrared (VNIR) data spatially. This is followed by the removal of stripes and pixel (column) dropouts and noise reduction, using recently developed automated software tools. The data cube is subsequently analyzed using keystone and spectral smile detection software to characterize these distortions. Included in the smile detection procedure is an optional gain and offset correction technique. The radiance data are converted to reflectance using a MODTRAN-based atmospheric correction procedure. Only at this point are the data corrected for smile effects. Any artifacts still remaining after these corrections are removed by post-processing.

05/01864 Kinetic study on the aggregation of coal solubleconstituents in solution: Shui, H. and Zhou, H. Fuel Processing Technology, 2005, 86, (6), 661–671

Integrated analysis of imaging spectrometer data and field spectral measurements were used in conjunction with conventional geologic field mapping to characterize bedrock and surficial geology at the northern end of Death Valley, California and Nevada. A knowledge-based expert system was used to automatically produce image maps showing the principal surface mineralogy from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Linear spectral unmixing of the AVIRIS data allowed further determination of relative mineral abundances and identification of mineral assemblages and mixtures. The imaging spectrometer data show the spatial distribution of spectrally distinct minerals occurring both as primary rockforming minerals and as alteration and weathering products. Field spectral measurements were used to verify the mineral maps and field mapping was used to extend the remote sensing results. Geographically referenced image maps produced from these data form new base maps from which to develop improved understanding of the prosesses of deposition and erosion affecting the present land surface.

Atmospheric effects on the classification of surface minerals in an arid region using Short-Wave Infrared (SWIR) hyperspectral imagery and a spectral unmixing technique

This study focuses on the comparison of spectral unmixing results from at-sensor radiance data and atmospherically corrected data, i.e., surface reflectance. The airborne Short-Wave Infrared (SWIR) Full Spectrum Imager (SFSI) and Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) were used to acquire data over Cuprite, Nevada, in June 1995 and 1996, respectively. This is an arid region that is well known for its exposed bedrock and alteration zones. For this project, a portion of the SWIR-2 spectral range covering the atmospheric window between 2050 and 2350 nm and a linear spectral unmixing technique were used to map surface minerals, including products of hydrothermal alteration. In total, eight end-members were extracted from the imagery using a new automatic procedure called Iterative Error Analysis (IEA). The atmospheric corrections were applied using a look-up-table procedure implemented in the Imaging Spectrometer Data Analysis System (ISDAS) and created with the radiative transfer model MODTRAN3. The fraction, or abundance, maps derived from the two types of data were compared using the coefficient of determination (R2) and the Average Euclidean Distance Coefficient (AEDC). Very good unmixing agreement was found between the results from the at-sensor radiance data and those from the surface reflectance data. For the SFSI data, the R2 values range from 0.72 to 0.95 and the AEDC values range from 0.098 to 0.023, whereas for the AVIRIS data, the R2 values range from 0.92 to 0.99 and the AEDC values range from 0.021 to 0.008. This suggests that, for the spectral range considered, atmospheric corrections are not necessary for mineral mapping in an arid region and for desert atmospheric conditions.

MAIS imaging spectrometer data analysis for Ni-Cu prospecting in ultramafic rocks of the Jinchuan group, China

The Jinchuan intrusion, situated in the Gansu province of China some 30 km NE of Yongcang city is an ultramafic dyke-like body emplaced in the Longshoushan uplifted terrain on the southwest margin of the Sino-Korea platform. The intrusion is 6 km long, 35 m wide and hosts a major Ni-Cu sulfide deposit. The main stratigraphic units include the highly metamorphosed Lower Proterozoic Longshoushan group and the less extensively metamorphosed Dongzigou and Hanmushan groups of Middle and Upper Proterozoic age. The Longshoushan group consists of serpentine marble, schist, gneiss and plagioclase amphibolite. The Dongzigou group comprises schists and granitic rocks. The Hanmushan group basically is represented by a large variety of volcanics. Small mafic-ultramafic intrusions are located along faults in the Jinchuan terrain. These ultramafic rock bodies show a concentric zoning with a core of dunites and olivine pyroxenites forming the margins of the bodies. Hydrothermal alteration (mainly serpentinization) has affected the rocks of the ultramafic bodies to a variable extent; the dunites are extensively altered whereas the pyroxene rich units are relatively fresh. Field spectroscopy is used to spectrally characterize the major rock units. Imaging spectrometer data was flown during the end of July until beginning of August of 1993 with a Chinese imaging spectrometer instrument called MAIS: the Modular Airborne Imaging Spectrometer. Feature characterization permits lithologic mapping from MAIS data. This aids in mineral potential mapping in the Jinchuan region and permits the identification of areas of possible Ni-Cu deposits.

Processing/analysis capabilities for data acquired with hyperspectral spaceborne sensors

The Imaging Spectrometer Data Analysis System (ISDAS) is being developed by the Canada Centre for Remote Sensing and MacDonald Dettwiler and Associates on Sun Microsystems SPARC workstations using the Application Visualization System (AVS) software package to meet the requirements for efficient processing and analysis of hyperspectral data acquired with airborne as well as future spaceborne sensors. Various visualization tools have been developed for rapid exploratory analysis together with preprocessing and information extraction tools for numerical analysis. Linkages to a spectral database and a conventional image analysis system were established to support the analysis. ISDAS is being used for multidisciplinary applications development in areas such as agriculture, environment, and exploration geology using physically based analysis approaches to retrieve information from hyperspectral data. This continuing effort, in collaboration with industry will lead to streamlined procedures that are important to take hyperspectral satellite remote sensing towards an operational mode.

The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data

The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides “point and click” operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).

Expert system-based mineral mapping in northern death valley, California/Nevada, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Integrated analysis of imaging spectrometer data and field spectral measurements were used in conjunction with conventional geologic field mapping to characterize bedrock and surficial geology at the northern end of Death Valley, California and Nevada. A knowledge-based expert system was used to automatically produce image maps showing the principal surface mineralogy from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Linear spectral unmixing of the AVIRIS data allowed further determination of relative mineral abundances and identification of mineral assemblages and mixtures. The imaging spectrometer data show the spatial distribution of spectrally distinct minerals occurring both as primary rockforming minerals and as alteration and weathering products. Field spectral measurements were used to verify the mineral maps and field mapping was used to extend the remote sensing results. Geographically referenced image maps produced from these data form new base maps from which to develop improved understanding of the prosesses of deposition and erosion affecting the present land surface.

Image processing software for imaging spectrometry data analysis

The advent of a new generation of remote sensing instruments, called imaging spectrometers, promises to provide scientists a greatly enhanced capability for detailed observations of the earth's surface. These instruments collect image data in literally hundreds of spectral channels simultaneously from the near ultraviolet through the short wavelength infrared, and are capable in many cases of providing direct surface materials identification in a manner similar to that used in laboratory reflectance spectroscopy. The volume and complexity of data produced by these instruments offers a significant challenge to traditional multispectral image analysis methods, and in fact requires the development of new approaches to efficiently manage and analyze these data sets. This paper describes a software system specifically designed to provide the science user with a powerful set of tools for carrying out exploratory analysis of imaging spectrometer data utilizing only modest computational resources.