Gamma Correction-Based Automatic Unsupervised Change Detection in SAR Images Via FLICM Model

In order to improve the accuracy of change detection, a novel synthetic aperture radar (SAR) image change detection method based on Gamma correction and fuzzy local information c-means clustering (FLICM) model is proposed in this paper. Firstly, the original SAR images are filtered by speckle reducing anisotropic diffusion filter; secondly, the difference image (DI) is obtained by log-ratio operator; thirdly, the DI is processed by the Gamma correction operation; finally, the FLICM model is used to get the change detection result. Experimental results on four groups of SAR images demonstrate that the proposed algorithm has a good performance than many competitive approaches in terms of SAR image change detection.