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Unsupervised SAR Image Change Detection Based on SIFT Keypoints and Region Information

机译：基于SIFT关键点和区域信息的无监督SAR图像变化检测

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摘要

This letter presents a new unsupervised distribution-free change detection method for synthetic aperture radar (SAR) images based on scale-invariant feature transform (SIFT) keypoints and region information. Since the SIFT can detect bloblike structures in an image and be insensitive to noise, we first extract noise-robust SIFT keypoints in the log-ratio image to reduce the detection range. Then, in order to obtain accurate changed regions, rather than directly obtaining the change-detection map from the difference image as in some traditional change detection methods, we make segmentation around the extracted keypoints in the two original multitemporal SAR images, where the edges of detection regions are much clearer than those in the difference image, and further compare the two segmentations to generate the change-detection map. This method utilizes the bloblike structure information offered by SIFT keypoints and the region information extracted via image segmentation. Experiments on real SAR images demonstrate the effectiveness of the proposed method.
机译：这封信提出了一种新的基于尺度不变特征变换（SIFT）关键点和区域信息的合成孔径雷达（SAR）图像无监督无变化检测方法。由于SIFT可以检测图像中的斑点状结构并且对噪声不敏感，因此我们首先在对数比图像中提取鲁棒的SIFT关键点以减小检测范围。然后，为了获得准确的变化区域，而不是像某些传统的变化检测方法那样直接从差异图像中获取变化检测图，我们围绕两个原始的多时间SAR图像中提取的关键点进行了分割，检测区域比差异图像中的区域要清晰得多，并进一步比较这两个分割以生成变化检测图。该方法利用了SIFT关键点提供的斑点状结构信息和通过图像分割提取的区域信息。在真实SAR图像上的实验证明了该方法的有效性。

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