Hidden Markov Model-based Spectral measure for Hyperspectral Image Analysis

摘要

A Hidden Markov Model (HMM)-based spectral measure is proposed. The basic 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. In order to evaluate the performance of this new measure, it is further compared to two commonly used spectral measures, Euclidean Distance (ED), Spectral Angle Mapper (SAM) and a recently proposed Spectral Information Divergence (SID). The experimental results show that the HMMID performs more effective than the other three measures in characterizing spectral information at the expense of computatonal complexity.