SAR Images Change Detection Based on Spatial Coding and Nonlocal Similarity Pooling

Accurate detection of the changed areas and effective speckle suppression are the main difficulties in synthetic aperture radar (SAR) image change detection (CD). The available feature extraction techniques for CD always ignore the spatial context correlation and are not robust to speckle noise. To overcome these drawbacks, we present a novel feature extraction technique that takes full advantage of sparse representation (SR) and nonlocal similarity of SAR images. First, each pixel in the difference image is represented by a feature vector, which is extracted using the sparse coding with a constructed robust discriminative dictionary. Next, a group of related feature vectors for each pixel can be generated according to the nonlocal similarity of SAR image. Finally, the discriminative change feature is obtained by means of the pooling, which can extract significant change information from the feature group. This method not only suppresses the speckle noise effectively but also improves the discrimination of the extracted features. The experimental results verify the superior performance of the proposed method on several real SAR image data sets and simulated image pairs.