Optimized algorithm for spectral band selection for rock-type classification

Abstract

Efficient use of hyperspectral (HS) sensors that can selectively activate individual, narrow spectral bands requires the development of optimized band-selection strategies that are adapted to the needs of specific detection and classification problems. By removing superfluous components of the HS data, optimized band selection significantly reduces the computational burden and improves robustness in classification. In this paper, a new method for selection of a subset of HS bands is proposed that is tailored to the problem of recognition of classes of rocks and minerals. Based on the analysis of the distribution of the amplitudes of the principal components (PCs) for a given training data set, this method identifies subsets of HS bands that provide highest spectral contrast. Three criteria are considered in the band selection process. The first criterion is based on ranking the HS bands according to the minimum distance among their respective PCs' amplitudes. The second criterion is based on ranking the HS bands according to a variant of the Kullback-Liebler divergence between a uniform distribution and the distribution of the amplitudes of the PCs for each HS band. This criterion assigns a high number on HS-bands whose PC-amplitudes' distribution either exhibits a wide range or a strong similarity to the uniform distribution. The third criterion is based on ranking the HS bands according to the empirical relative variances of the inter-amplitude distances of successive amplitudes at each HS band; it ranks high those HSbands with small inter-amplitude-distance variance and large amplitude range. These band-selection strategies are applied to laboratory HS data of rocks and minerals yielding a subset of thirteen optimal multispectral bands. The classification performance with the reduced number of bands is compared with that of the 13-band Multispectral Thermal Imager showing a moderate improvement in classification.

Statistical adaptive sensing by detectors with spectrally overlapping bands

“…[ [10]] On the basis of the spectral overlap and diversity of QDIPs, approaches that use principal components analysis and canonical correlation analysis have been developed to study the performance of sensing. [ [17], [18], [19], [20], [21], [22]] The detectors' responsivities, which are functions of wavelength, are transformed into an uncorrelated function space. Since the spectra are practically sampled in wavelength, they have viewed sensing as an inner product between the sampled scene spectrum vector and the detector responsivity vector.…”