Statistical Difference Models for Change Detection in Multispectral Images

This chapter aims to present a general mathematical framework for the representation and analysis of multispectral images. It introduces two statistical models for the description of the distribution of spectral difference-vectors, and provides from them change detection methods based on image difference. The chapter presents an overview of the change detection problem in multispectral imagery and the methods proposed in the literature to address it, with emphasis on the statistical models associated with the difference image and their challenges. It also introduces the standard two-class unchange/change model for binary change detection, as derived from the hypothesis of the Gaussian distribution of natural classes in the difference image. Experiments on different image pairs from different sensors confirmed that the improved fitting of the magnitude histogram corresponds to nearly optimal change detection accuracy.