Analysis of Imaging Spectrometer Data Using $N$-Dimensional Geometry and a Mixture-Tuned Matched Filtering Approach
- 15 August 2011
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 49 (11), 4138-4152
- https://doi.org/10.1109/tgrs.2011.2161585
Abstract
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.Keywords
This publication has 33 references indexed in Scilit:
- Improving Subpixel Classification by Incorporating Prior Information in Linear Mixture ModelsIEEE Transactions on Geoscience and Remote Sensing, 2010
- Linear Spectral Mixture Analysis Based Approaches to Estimation of Virtual Dimensionality in Hyperspectral ImageryIEEE Transactions on Geoscience and Remote Sensing, 2010
- Magnitude- and Shape-Related Feature Integration in Hyperspectral Mixture Analysis to Monitor Weeds in Citrus OrchardsIEEE Transactions on Geoscience and Remote Sensing, 2009
- Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix FactorizationIEEE Transactions on Geoscience and Remote Sensing, 2007
- A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agentsIEEE Transactions on Geoscience and Remote Sensing, 2006
- Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mappingIEEE Transactions on Geoscience and Remote Sensing, 2003
- The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer dataRemote Sensing of Environment, 1993
- Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the northern grapevine mountains, Nevada, and CaliforniaRemote Sensing of Environment, 1988
- A transformation for ordering multispectral data in terms of image quality with implications for noise removalIEEE Transactions on Geoscience and Remote Sensing, 1988
- Imaging Spectrometry for Earth Remote SensingScience, 1985