A Statistical Analysis for WavelengthResolution SAR Image Stacks This letter presents a clutter statistical analysis for stacks of wavelengthresolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and lognormal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelengthresolution SAR images, where 98.59% of the tested samples passed the Andersonxe2x80x93Darling (AD) goodnessoffit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.

Practical Considerations in Unsupervised Change Detection Using SAR Images Change detection has a wide range of applications in hurricane damage assessment, urban growth monitoring, etc. It is wellknown that synthetic aperture radar (SAR) can penetrate clouds and can work well under all weather conditions. However, SAR images also contain a lot of speckle noise that seriously affects change detection performance. In this paper, we focus on change detection using SAR images. In particular, we propose a new change detection algorithm that is applicable to both optical and SAR images. The detection performance of the algorithm was compared with a number of existing algorithms in the literature. Preliminary results using actual SAR images are encouraging. Most importantly, it was observed that effective change detection using SAR images require good denoising and postprocessing algorithms in order to achieve decent performance.