1 December 2002 Unsupervised change-detection methods for remote-sensing images
Author Affiliations +
Optical Engineering, 41(12), (2002). doi:10.1117/1.1518995
Abstract
An unsupervised change detection problem can be viewed as a classification problem with only two classes corresponding to the change and no-change areas, respectively. Thanks to its simplicity, image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vector associated with each pixel in the study area. To separate the "change" and "no-change" classes in the difference image, a simple thresholding-based procedure can be applied. However, the selection of the best threshold value is not a trivial problem. We investigate and compare several simple thresholding methods. The combination of the expectation-maximization algorithm with a thresholding method is also performed for the purpose of achieving a better estimate of the optimal threshold value. As an experimental investigation, a study area damaged by a forest fire is considered. Two Landsat TM images of the area acquired before and after the event are utilized to detect the burnt zones and to assess and compare the mentioned unsupervised change-detection methods.
Farid Melgani, Gabriele Moser, Sebastiano Bruno Serpico, "Unsupervised change-detection methods for remote-sensing images," Optical Engineering 41(12), (1 December 2002). http://dx.doi.org/10.1117/1.1518995
JOURNAL ARTICLE
10 PAGES


SHARE
KEYWORDS
Expectation maximization algorithms

Principal component analysis

Image classification

Vegetation

Statistical analysis

Optical engineering

Remote sensing

Back to Top