Classification of multi-spectral remote sensing images based on hidden Markov models

Hui Wang; Jian Lu

doi:10.1117/12.654982

3 November 2005 Classification of multi-spectral remote sensing images based on hidden Markov models

Hui Wang, Jian Lu

Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 604325 (2005) https://doi.org/10.1117/12.654982
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

This paper presents the analogy between voice recognition and multi-spectral remote sensing image classification, and introduces the Hidden Markov Model (HMM), which is a successful approach on voice recognition fields, into multi-spectral remote sensing image classification. After comparing the HMM with other conventional classification methods such as Maximum Likelihood and Minimum Distance, the paper concludes that the HMM is a better approach than other techniques do. At the end of the paper, the author explains the reason of HMM's good performance, and also points out its defect.

Citation Download Citation

Hui Wang and Jian Lu "Classification of multi-spectral remote sensing images based on hidden Markov models", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 604325 (3 November 2005); https://doi.org/10.1117/12.654982

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