Practical Considerations in Unsupervised Change Detection Using SAR Images Change detection has a wide range of applications in hurricane damage assessment, urban growth monitoring, etc. It is wellknown that synthetic aperture radar (SAR) can penetrate clouds and can work well under all weather conditions. However, SAR images also contain a lot of speckle noise that seriously affects change detection performance. In this paper, we focus on change detection using SAR images. In particular, we propose a new change detection algorithm that is applicable to both optical and SAR images. The detection performance of the algorithm was compared with a number of existing algorithms in the literature. Preliminary results using actual SAR images are encouraging. Most importantly, it was observed that effective change detection using SAR images require good denoising and postprocessing algorithms in order to achieve decent performance.

TwoPhase ObjectBased Deep Learning for Multitemporal SAR Image Change Detection. Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a much negative effect on change detection. In this research, a novel twophase objectbased deep learning approach is proposed for multitemporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighboring pixels into segmented into superpixel objects (from pixels) such as to exploit local spatial context. Two phases are designed in the methodology: 1) Generate objects based on the simple linear iterative clustering algorithm, and discriminate these objects into changed and unchanged classes using fuzzy cmeans (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. 2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multitemporal SAR imagery.