Target-driven change detection based on data transformation and similarity measures

This paper presents a novel unsupervised target-driven change detection procedure for analyzing multi-temporal remote sensing images, which is based on data transformation and similarity measures. The iteratively reweighted multivariate alteration detection (IR-MAD) technique is firstly used to separate the various change information into MAD components. Then, the similarity measures are used to automatically search for the target-related component according to a pre-defined target-driven rule. This procedure both takes advantage of the IR-MAD transformation in change detection and helps users to quickly locate the transformed component associated with their interesting change target. Experimental results obtained on multitemporal Landsat ETM+ data confirm the effectiveness of the proposed approach.