A Change Detector Based on an Optimization With Polarimetric SAR Imagery

The possibility to detect changes in land cover with remote sensing is particularly valuable considering the current availability of long time series of data. Synthetic Aperture Radar (SAR) can play an important role in this context since it can acquire complete time series without limitations of cloud cover. Additionally, polarimetry has the potential to improve significantly the detection capability, allowing the discrimination between different polarimetric targets. This paper is focused on developing two new methodologies for testing the stability of observed targets (i.e., equiscattering-mechanism hypothesis) and change detection. Both the algorithms adopt a Lagrange optimization, which can be performed with two eigenproblems. Interestingly, the two optimizations share the same eigenvectors. Three statistical tests are proposed to set the threshold for the change detector. Two of them are mostly aimed at point targets, and one is more suited for distributed targets.