Development and processing of hyperspectral images in optical–electronic remote sensing systems

Abstract

The development and processing of three-dimensional images as a “hypercube” of spectral data in hyperspectral optical–electronic remote sensing systems are described in a formalized manner. The correlation identification of observed objects on the basis of spectral features is considered. The criterion for determining of similarity between vectors of recorded and reference spectral images of objects is based on their cross-correlation. Taking into the fact that the total spectral data array recorded by currently applicable hyperspectrometers is excessive for the solution of many issues related to remote sensing of the Earth, this paper proposes a method making it possible to reduce spectral data redundancy by selection of the most informative spectral channels. The essential dimension of the spectral data makes it possible to solve issues related to identification and classification of objects by spectral features through a limited number of very informative spectral channels selected in the areas where the function describing a spectral image of the observed object undergoes well-defined changes in behavior. The algorithm for selection of the most informative spectral channels, which is based on the determination of jump coordinates (major changes) of a spectral image, is substantiated. The selected channels meet the maximum likelihood criterion. The obtained experimental research data on object identification quality with involvement of real hyperspectral data of aerospace Earth remote sensing systems are reported. Five to twenty spectral readouts are needed to provide identification by a limited number of very informative spectral channels. This confirms the idea of existing essential dimensionality of the spectral data.