Multispectral image classification using a mixture density model

Sylvia S. Shen; Brian D. Horblit

doi:10.1117/12.130835

16 December 1992 Multispectral image classification using a mixture density model

Sylvia S. Shen, Brian D. Horblit

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130835
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

This paper describes a multispectral image classification technique. This technique involves two steps. First, we describe the underlying distribution of the pixel intensity vectors for the entire scene as a mixture of multivariate Gaussian distributions. We then use this mixture decomposition and a small number of labeled pixels to estimate the proportion of a mixture component that is comprised of a certain class, which enables us to use a Bayes-type decision rule to classify each pixel in the scene. Results of applying this technique to three-band SPOT data are presented. Comparisons with results obtained from a maximum likelihood classifier are also presented.

Citation Download Citation

Sylvia S. Shen and Brian D. Horblit "Multispectral image classification using a mixture density model", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130835

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