Synthetic Aperture Radar Remote Sensing images change detection based on SWT and DWT with Fuzzy C Means clustering

Abstract

The collection of techniques involved in monitoring objects on the Earth’s surface without any physical contact is called remote sensing. It is well- known fact that the speckle noise is existed in SAR images. After the minimization of speckle noise, discrete wavelet (DWT) fusion is exploited for further image segmentation. As it is very familiar that more image information could be brought up by using image fusion process. Filter coefficients have been chosen in the DWT (Discrete Wavelet Transform) filter bank to perform DWT and SWT.  The change detection in remote sensing images is the process of detecting changes that take place between two SAR (Synthetic Aperture Radar) images gathered at dissimilar periods of the instance from same geographical area. The two or more SAR images obtained from the identical region at diverse time points are detected in terms of changes in accordance with the ground. Thus, the change detection mechanism is being widely employed in the field of military reconnaissance, natural calamity evaluation, and observing on marine oil spill, forest fire and urban expansion. In this paper Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform techniques have been employed for the fusion process and the fused image is segmented. Fuzzy C – Means clustering is used for segmentation and the segmented image is compared with the ground truth image. The performance of this method is measured in terms of measuring parameters accuracy, FDR, sensitivity etc.,