期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 5, 期 2, 页码 256-260出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2008.915934
关键词
band selection; dimensionality reduction; endmember; hyperspectral imagery; sparsity promotion
类别
资金
- U.S. Army Research Office
- U.S. Army Research Laboratory [DAAD19-02-2-0012]
- U.S. Government
This letter presents a simultaneous band selection and endmember detection algorithm for hyperspectral imagery. This algorithm is an extension of the sparsity promoting iterated constrained endmember (SPICE) algorithm. The extension adds spectral band weights and a sparsity promoting prior to the SPICE objective function to provide integrated band selection. In addition to solving for endmembers, the number of endmembers, and endmember fractional maps, this algorithm attempts to autonomously perform band selection and to determine the number of spectral bands required for a particular scene. Results are presented oil a simulated data set and the AVIRIS Indian Pines data set. Experiments on the simulated data set show the ability to find the correct endmembers and abundance values. Experiments on the Indian Pines data set show strong classification accuracies in comparison to previously published results.
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