Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications

Change detection of remotely sensed images is a particularly challenging task when the time series data come from different sensors. Indeed, many change indicators are based on radiometry measurements, used to calculate differences or ratios, that are no longer meaningful when the data have been acquired by different instruments. For this reason, it is interesting to study those indicators that do not rely completely on radiometric values. In this work a new approach is proposed based on similarity measures. A series of such measures is employed for automatic change detection of optical and SAR images and a comparison of their performance is carried out to establish the limits of their applicability and their sensitivity to the occurred changes. Initial results are promising and suggest similarity measures as possible change detectors in multi-sensor configurations.