Detection of changes in synthetic aperture radar images using Modified Gauss-Log ratio and Fuzzy Local Information C-Means clustering.

Change detection in SAR is currently known to be a happening field of research in the domain of computer vision and remote sensing. There are numerous approaches and techniques available to detect the change in varied types of images captured in various areas. In this paper, we present a technique for detecting changes in SAR images, which generally happen to be poor contrast and poor brightness grayscale images. Consequently, they are complicated to change detection. We developed a change detection strategy in this study that employs a Convolution Neural Network (CNN) as a classification model. Further, we have used the Fuzzy Local Information C-Means approach to find out interesting pixels which have a high possibility of being changed or unchanged. By integrating the CNN classification result with the pre-classification result, the final change map is created. In this paper, we have tried to generate some virtual samples to supplement the lack of training samples. The efficiency and robustness of the planned approach have been ascertained with investigational results on four real SAR image data sets compared to several existing methodologies. [ABSTRACT FROM AUTHOR]