Abstract
This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV) derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ) and successive (SC) algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.
Highlights
IntroductionHyperspectral image classification has received considerable interest in recent years [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]
Of particular interest is a new concept of band subset selection (BSS) to address this issue which is quite different from the aforementioned simultaneous multiple band selection (SMMBS) methods in the sense of the search strategy to be used for finding an optimal set of multiple bands
Another SMMBS approach is to narrow the search range by specifying particular parameters to limit a small number of band subsets as candidate optimal sets, follow an optimization algorithm such as particle swarm optimization (PSO) [35] or firefly algorithm (FA) [36] to find an optimal band subset from the selected candidate set of band subsets
Summary
Hyperspectral image classification has received considerable interest in recent years [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. One type of BP criterion is based on data characteristics or statistics such as variance, signal-to-noise ratio (SNR), entropy, and information divergence (ID) to calculate a priority score for each of the individual bands in order to rank them [25] As a result, such BP-based SQMBS is generally unsupervised and is not adaptive to any particular application. Of particular interest is a new concept of band subset selection (BSS) to address this issue which is quite different from the aforementioned SMMBS methods in the sense of the search strategy to be used for finding an optimal set of multiple bands. It considers a selected band as a desired endmember. This is a tremendous advantage since such parameters must be adaptive to various applications
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