Hidden Markov model approach to spectral analysis for hyperspectral imagery

被引:15
作者
Du, Q [1 ]
Chang, CI
机构
[1] Texas A&M Univ, Dept Elect Engn & Comp Sci, Kingsville, TX 78363 USA
[2] Univ Maryland Baltimore Cty, Dept Comp Sci & Comp Engn, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA
关键词
Euclidean distance; hidden Markov model; hidden Markov model information divergence; hyperspectral images; spectral angle mapper; spectral characterization; spectral information divergence;
D O I
10.1117/1.1404430
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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 HMM-based 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. (C) 2001 Society of Photo-Optical Instrumentation Engineers.
引用
收藏
页码:2277 / 2284
页数:8
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