Abstract: Change Detection is a process that analyzes images of the same scene taken at different times in order to identify changes that may have occurred between the considered acquisition dates. In the last decades, it has attracted widespread interest due to a large number of applications in diverse disciplines such as remote sensing, medical diagnosis, and video surveillance. The proposed method is unique in the following two aspects: 1) producing difference images by fusing a mean-ratio image and a log-ratio image and 2) improving the fuzzy local-information c-means (FLICM) clustering algorithm, which is insensitive to noise, to identify the change areas in the difference image, without any distribution assumption. With the development of remote sensing technology, change detection in remote sensing images becomes more and more important. Among them, change detection in synthetic aperture radar (SAR) images exhibits some more difficulties than optical ones due to the fact that SAR images suffer from the presence of the speckle noise. However, SAR sensors are independent of atmospheric and sunlight conditions, which make the change detection in SAR images still attractive. SAR-image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method.
Keywords: Change detection (CD), mean-ratio image, Fuzzy local-information c-means (FLICM) clustering algorithm.