Multi-sensor Remote Sensing Image Change Detection: An Evaluation of Similarity Measures

Change detection from remote sensing imagery is of great interest in disaster management, surveillance, and other applications. Most of the existing approaches are pixel based and rely on direct comparison of radiometric values to detect changes. Such techniques are susceptible to atmospheric conditions, noise, and registration errors. In this paper, we evaluate change detection approaches using several similarity measures that does not rely entirely on radiometric values of the images. Our hypothesis is based on the assumption, that even though images are obtained from different sensors and at different times, the underlying basis in the scene is still the same, since they are different representations of the same reality. In other words, different sensors capture overlapping information in different forms. Thus, we expect that similarity measures provides contrasting information for change vs. no change patches. We evaluated several measures and experimental results show the effectiveness of each measure in identifying the changed regions in the images.