SAR Image Change Detection Using Saliency Extraction and Shearlet Transform

Change detection has recently become a topic of great significance in the field of remote sensing. However, as one of the traditional effective detection methods, synthetic aperture radar (SAR) image change detection based on wavelet transform fusion remains limited because of the existence of speckle noise and because multidirectional information has been underutilized. Therefore, we propose an unsupervised change detection method using saliency extraction and the shearlet transform. Saliency extraction is first used to homogenize registered images to reduce speckle noise. Using a subtraction operation for preprocessed binary images, we then obtain a saliency-guided difference image (DI) that includes the main contour change information. Then, the Gauss-log-ratio DI includes the detailed change information at the edge of the image and acts as an auxiliary DI. Next, two DIs are fused with the shearlet transform. During this process, the DI is decomposed into one low-frequency and four high-frequency subimages. The low-frequency subimage contains image contour information and the high-frequency subimages contain the edge information of the images. Compared to wavelet fusion, in our method, no extra fusion noise occurs because the shearlet transform performs multiscale analysis. The final change map can be obtained through maximum entropy segmentation. Real SAR image pairs in areas of Bern, Switzerland, and Suzhou, China, are used to verify the proposed change detection method. The experimental results demonstrate the effectiveness of the proposed method when compared to the reference methods.