Change detection (CD) is one of the most important application in remote sensing domain. The difference image (DI) generated by traditional change detection methods are sensitive to several factors, such as atmospheric condition changes, illumination variations, sensor calibration, and speckle noise, greatly affecting the detection performance. To avoid the aforementioned problem, in this paper, a novel approach based on superpixel segmentation and image regression is proposed to detect changes between bitemporal synthetic aperture radar (SAR) images. Specifically, the bitemporal images are firstly divided into a number of superpixel pairs under the guidance of segmentation result of a pre-DI. Next, each pixel in pre-event image is reconstructed utilizing its nearest neighbor to reduce the influence of noise. Then, a set of preselected unchanged sample are selected to learn the local regression model and to estimate the post-event image. After that, the final DI can be obtained by measuring the difference between estimated post-event image and the actual one. Finally, the fuzzy c-means (FCM) clustering algorithm is adopted to generate the binary change map. Adequate experiments on four SAR datasets have been tested, and the experimental results compared with the state-of-the-art methods have proved the superiority of the proposed method.

变更检测（CD）是遥感领域最重要的应用之一。传统变化检测方法生成的差异图像（DI）对多种因素敏感，例如大气条件变化，照度变化，传感器校准和斑点噪声，极大地影响了检测性能。为了避免上述问题，本文提出了一种基于超像素分割和图像回归的新颖方法来检测SAR图像之间的变化。具体地，在pre-DI的分割结果的指导下，首先将时空图像划分为多个超像素对。接下来，利用事件前图像中的最近像素来重建事件前图像中的每个像素，以减少噪声的影响。然后，选择一组预选的不变样本以学习局部回归模型并估计事后图像。之后，可以通过测量估计的事件后图像与实际图像之间的差异来获得最终的DI。最后，采用模糊c均值（FCM）聚类算法生成二进制变化图。已经对四个SAR数据集进行了充分的实验，并将实验结果与最新方法进行比较，证明了该方法的优越性。