Change detection in multiple-temporal Synthetic Aperture Radar images based on averaged heterogeneous factors of neighbourhood areas

Change detection in multiple-temporal Synthetic Aperture Radar images has been received great interests for recent decades. The basic principle of change detection is to analyse the difference images generated from two Synthetic Aperture Radar images captured in the same geographic area at two different times. The popular operators used to create difference images are traditional subtraction, ratio, logarithm based ones and modified versions of them, which can use pixel information in the local or global areas. A challenge in detecting changes is to reduce impacts of speckle noises inherently existing in Synthetic Aperture Radar images on the accuracy of the detection. This paper proposed a novel algorithm to create the difference images based on averaging heterogeneous factors of corresponding neighbourhood areas in the two images. The resultant difference image is then filtered by the average filter to reject remaining speckle noises.