Detecting a step pattern of change in multitemporal SAR images

We address the problem of deriving adequate detection and classification schemes to fully exploit the information available in a sequence of SAR images. In particular we address the case of detecting a step reflectivity change pattern against a constant pattern. We propose two different techniques, based on a maximum likelihood approach, that make different use of prior knowledge on the searched pattern. They process the whole sequence to achieve optimal discrimination capability between regions affected and not affected by a step change. The first technique assumes a complete knowledge of the pattern of change, while the second one is based on the assumption of totally unknown pattern. A fully analytical expression of the detection performance of both techniques is obtained, which shows the large improvement achievable using longer sequences instead of only two images. The practical effectiveness of the technique on real data is shown by applying the detector for unknown step pattern to a sequence of 10 ERS-1 SAR images of forest and agricultural areas, which is also used to validate the theoretical results