23 September 2003 A physically constrained localized linear mixing model for TERCAT applications
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Abstract
A physically-constrained localized linear mixing model suitable to process multi/hyperspectral imagery for Terrain Categorization (TERCAT) applications is investigated. Unlike the basic spectral linear mixing model that typically includes all potential endmembers in a set, simultaneously, in the model for each site in an image, the proposed approach restricts the local model at each site to a subset of endmembers, using localized spectral/spatial constraints to narrow the selection process. This approach is used to reduce the observed instability of conventional linear mixture analysis in addressing TERCAT problems for scenes with a large number of endmembers. Experiments are conducted on an 18 channel GERIS scene, airborne-collected over Northern Virginia, that contains a diverse range of terrain features, showing the benefit of this method as compared to the basic linear mixture analysis approach for TERCAT applications.
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Robert S. Rand, Robert S. Rand, "A physically constrained localized linear mixing model for TERCAT applications", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.485935; https://doi.org/10.1117/12.485935
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