An Automatic Approach to the Unsupervised Detection of Multiple Changes in Multispectral Images

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. © 2010 Copyright SPIE - The International Society for Optical Engineering.