A COMBINATION OF NEIGHBORHOOD BASED RATIO OPERATOR AND CONVOLUTIONAL WAVELET NEURAL NETWORKS FOR CHANGE DETECTION IN MULTI-TEMPORAL SYNTHETIC APERTURE RADAR IMAGES

Abstract

Change detection in multi-temporal Synthetic Aperture Radar (SAR) images is widely utilized for practical applications of resource investigation, supervision, and management on sea and land with a large area. There is a variety of algorithms for the change detection using two multi-temporal SAR images. The popular principle of them is to analyze a difference image generated from these two images by a ratio operator to detect change areas between them. In order to improve detection accuracy, the ratio operator and modified versions of this operator are usually used in a combination with further fine processing solutions. This paper developed a novel solution of the change detection based on a combination of the Neighbor-based Ratio operator and the Convolutional Wavelet Neural Network algorithm for improving the accuracy of change detection in multi-temporal SAR images.