Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

In this paper, a novel change detection approach based on multi-grained cascade forest (gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detects the changed and unchanged areas of the images by using the well-trained gcForest. Most existing change detection methods need to select the appropriate size of the image block. However, the single size image block only provides a part of the local information, and gcForest cannot achieve a good effect on the image representation learning ability. Therefore, the proposed approach chooses different sizes of image blocks as the input of gcForest, which can learn more image characteristics and reduce the influence of the local information of the image on the classification result as well. In addition, in order to improve the detection accuracy of those pixels whose gray value changes abruptly, the proposed approach combines gradient information of the difference image with the probability map obtained from the well-trained gcForest. Therefore, the image edge information can be enhanced and the accuracy of edge detection can be improved by extracting the image gradient information. Experiments on four data sets indicate that the proposed approach outperforms other state-of-the-art algorithms.