Hidden Markov model approach to spectral analysis for hyperspectral imagery

Abstract

The hidden Markov model (HMM) has been widely used in speech recognition where it models a speech signal as a doubly stochastic process with a hidden state process that can be observed only through a sequence of observations. We present a new application of the HMM in hyperspectral image analysis inspired by the analogy between the temporal variability of a speech signal and the spectral variability of a remote sensing image pixel vector. The idea is to model a hyperspectral spectral vector as a stochastic process where the spectral correlation and band-to-band variability are modeled by a hidden Markov process with parameters determined by the spectrum of the vector that forms a sequence of observations. With this interpretation, a new HMMbased spectral measure, referred to as the HMM information divergence (HMMID), is derived to characterize spectral properties. To evaluate the performance of this new measure, it is further compared to two commonly used spectral measures, Euclidean distance (ED) and the spectral angle mapper (SAM), and the recently proposed spectral information divergence (SID). The experimental results show that the HMMID performs better than the other three measures in characterizing spectral information at the expense of computational complexity.