Performance Estimation of Similarity Measures of Multi-Sensor Images for Change Detection Applications

Change detection of remotely sensed images is a particularly challenging task when the available data come from different sensors. Indeed, many change indicators are based on radiometry measures, operating on them differences or ratios, that are no longer reliable when the data have been acquired by different instruments. For this reason, it is interesting to study the performance of those indicators that do not rely completely on radiometric values. A series of similarity measures for automatic change detection was investigated and their performance compared using optical and SAR images covering a period of several years. We could observe that the considered change detection algorithms perform differently but that none of them permits an "absolute" measure of the changes independent of the sensor.