Spectral feature probabilistic coding for hyperspectral signatures

Abstract

Spectral signature coding has been used to characterize spectral features where a binary code book is designed to encode an individual spectral signature and the Hamming distance is then used to perform signature discrimination. The effectiveness of such a binary signature coding largely relies on how well the Hamming distance can capture spectral variations that characterize a signature. Unfortunately, in most cases, such coding does not provide sufficient information for signature analysis, thus it has received little interest in the past. This paper reinvents the wheel by introducing a new concept, referred to as spectral feature probabilistic coding (SFPC) into signature coding. Since the Hamming distance does not take into account the band-to-band variation, it can be considered as a memoryless distance. Therefore, one approach is to extend the Hamming distance to a distance with memory. One such coding technique is the well-known arithmetic coding (AC) which encodes a signature in a probabilistic manner. The values resulting from the AC is then used to measure the distance between two signatures. This paper investigates AC-based signature coding for signature analysis and conducts a comparative analysis with spectral binary coding.