Spectral Feature Probabilistic Coding for Hyperspectral Signatures

Abstract

Spectral signature coding (SSC) is generally performed by encoding spectral values of a signature across its spectral coverage followed by the Hamming distance to measure signature similarity. The effectiveness of such an SSC largely relies on how well the Hamming distance can capture spectral variations that characterize a signature. Unfortunately, in most cases, this Hamming-distance-based SSC does not provide sufficient discriminatory information for signature analysis because the Hamming distance does not take into account the band-to-band variation, in which case the Hamming distance can be considered as a memoryless distance. This paper extends the Hamming-distance-based SSC to an approach, referred to as spectral feature probabilistic coding (SFPC), which introduces a new concept into SSC that uses a criterion with memory to measure spectral similarity. It implements the well-known arithmetic coding (AC) in two ways to encode a signature in a probabilistic manner, called circular SFPC and split SFPC. The values resulting from the AC is then used to measure the distance between two spectral signatures. In order to demonstrate advantages of using AC-based SSC in signature analysis, a comparative analysis is also conducted against spectral binary coding.