Port surveillance by using co-occurrence matrix on multi-temporal SAR images

Abstract

In this paper, we propose an unsupervised change detection method using the labeled co-occurrence matrix on multitemporal SAR images. In SAR images, each land cover (LC) class has a distinct reflectivity to radar signals and presents a specific backscattering value. Generally, the amplitude of the SAR images can be roughly clustered into three classes according to the backscattering behaviors of the LC classes. The changes occurred between the images can be considered as a backscattering variation that is changed from one backscattering class into another. As a result, we analyzed the possible cases of the positive and negative backscattering variations, and merged the initial three backscattering classes into two classes with the pixel in the medium backscattering class being attached to the strong backscattering class and the low backscattering class respectively in a membership degree. Two pairs of fuzzy-label images are derived accordingly, where each pair of fuzzy-label images are computed from the multi-temporal SAR data. The labeled cooccurrence matrix is computed locally on each pair of fuzzy-label images by combining the membership values in a conjunctive operator, and the autocorrelation feature is extracted. The classifications are implemented by Otsu Nthresholding algorithm on the derived two autocorrelation features. The final binary change detection map is achieved by combining the obtained two classification results. Experiments were carried on portions of multi-temporal Radarsat-1 SAR data. The effectiveness of the proposed approach was confirmed.