SAR image change detection based on saliency region guidance and SIFT keypoint extraction

Abstract

Synthetic Aperture Radar (SAR) can operate under all-weather, all-day conditions, playing a crucial role in regional change detection (CD). However, due to its unique imaging principles, SAR images contain significant speckle noise and blurred boundary and detail features, which reduces the detection accuracy and leads to missed detection and false detection. To address these issues, this paper proposes a SAR image CD method based on saliency region guidance and Scale-Invariant Feature Transform (SIFT) keypoint extraction to reduce the interference of speckle noise. First, a saliency region guidance method is introduced to analyze the saliency of local features in SAR images, extracting potentially changed regions and reducing the interference of speckle noise. Second, the SIFT is employed to extract keypoints in regions significantly different from the background in the difference map, leveraging its robustness to speckle noise. By extracting keypoints, the approximate location and extent of the changed regions are determined. These are, then, fused with the saliency region information, enhancing the saliency weights of pixels around keypoints for more extraction of change regions. Finally, a Vision Transformer (ViT) detection network is used for SAR image CD, utilizing the combined saliency information from the original saliency map and SIFT keypoints. This approach effectively integrates SIFT’s stable description of local features with ViT’s modeling capability for global features, improving the model’s accuracy and robustness.