Hidden Markov model-based spectral measure for hyperspectral image analysis

Qian Du; Chein-I Chang

doi:10.1117/12.410361

23 August 2000 Hidden Markov model-based spectral measure for hyperspectral image analysis

Qian Du, Chein-I Chang

Proceedings Volume 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI; (2000) https://doi.org/10.1117/12.410361
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

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 computational complexity.

Citation Download Citation

Qian Du and Chein-I Chang "Hidden Markov model-based spectral measure for hyperspectral image analysis", Proc. SPIE 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, (23 August 2000); https://doi.org/10.1117/12.410361

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available