An automatic approach to the unsupervised detection of multiple changes in multispectral images

F. Bovolo; S. Marchesi; L. Bruzzone

doi:10.1117/12.866032

22 October 2010 An automatic approach to the unsupervised detection of multiple changes in multispectral images

F. Bovolo, S. Marchesi, L. Bruzzone

Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 78300T (2010) https://doi.org/10.1117/12.866032
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

In this paper we present a technique for the detection of multiple changes in multitemporal and multispectral remote sensing images. The technique is based on: i) the representation of the change detection problem in polar coordinates; and ii) a 2-step decision strategy. First of all the change information present in the multitemporal dataset is represented taking advantage from the framework for change detection in polar coordinates. Within this representation the Bayesian decision theory is applied twice: the first time for distinguishing changed from unchanged pixels; and the second one for discriminating different kinds of change within changed pixels. The procedure exploits the Expectation-Maximization algorithm and is completely automatic and unsupervised. Experiments carried out on high and very high resolution multispectral and multitemporal datasets confirmed the effectiveness of the proposed approach.

Citation Download Citation

F. Bovolo, S. Marchesi, and L. Bruzzone "An automatic approach to the unsupervised detection of multiple changes in multispectral images", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300T (22 October 2010); https://doi.org/10.1117/12.866032

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