An Automatic High Confidence Sets Selection Strategy for SAR Images Change Detection

Change detection result is usually obtained by clustering or classifying; however, the spatial information of pixels is rarely considered during the classification process. In this letter, we propose a practical method to improve the performance of existing change detection algorithms on remote-sensing images without prior information. First, the existing detection result is regarded as an initial result. Second, it takes advantage of this initial result with neighborhood information of pixels to select the training data, then a random forest classifier is trained for precise classification. Finally, the median filtering is used to eliminate singular points for further improvement of detection performance. Corresponding experiments on three real synthetic aperture radar (SAR) data sets demonstrate the effectiveness of the proposed method.