Abstract
Synthetic aperture radar (SAR) is a form of imaging radar. The defining characteristic of SAR is the usage of relative motion between an antenna and its target region to provide long-term coherent-signal variations that can be exploited to obtain fine spatial resolution for radar image. Due to the improvement of SAR data acquisition technique and the flexibility of SAR sensor deployment, multi-pass SAR imageries can be easily obtained. More information and intelligence can be extracted from SAR imageries. This paper will address one challenging issue for SAR data fusion, i.e. multi-pass SAR change detection. Two methods are proposed in this paper to perform multi-pass SAR change detection. One method is based on robust principal component analysis (PCA) and the other method uses template matching plus thresholding. Both methods explore the local statistics to indicate the change of each pixel in the SAR imageries. The visualization performances illustrate the potential of these two methods for the issue of SAR change detection.
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