As two different tools for earth observation, the optical and synthetic aperture radar (SAR) images can provide complementary information of the same land types for better land cover classification. However, because of the different imaging mechanisms of optical and SAR images, how to efficiently exploit the complementary information becomes an interesting and challenging problem. In this article, we propose a novel multimodal bilinear fusion network (MBFNet), which is used to fuse the optical and SAR features for land cover classification. The MBFNet consists of three components: the feature extractor, the second-order attention-based channel selection module (SACSM), and the bilinear fusion module. First, in order to avoid the network parameters tempting to ingratiate dominant modality, the pseudosiamese convolutional neural network (CNN) is taken as the feature extractor to extract deep semantic feature maps of optical and SAR images, respectively. Then, the SACSM is embedded into each stream, and the fine channel-attention maps with second-order statistics are obtained by bilinear integrating the global averagepooling and global max-pooling information. The SACSM can not only automatically highlight the important channels of feature maps to improve the representation power of networks, but also uses the channel selection mechanism to reconfigure compact feature maps with better discrimination. Finally, the bilinear pooling is used as the feature-level fusion method, which establishes the second-order association between two compact feature maps of the optical and SAR streams to obtain the low-dimension bilinear fusion features for land cover classification. Experimental results on three broad coregistered optical and SAR datasets demonstrate that our method achieves more effective land cover classification performance than the state-of-the-art methods. Index Terms-Attention mechanism, bilinear pooling model, convolutional neural network (CNN), feature fusion, land cover classification, multimodal learning. Xiao Li received the M.S. degrees in control science and engineering from Xiangtan University, Xiangtan, China, in 2018. He is currently working toward the Ph.D. degree in information and communication engineering from the

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Change detection (CD) has found a wide range of applications in many fields. In this article, we propose a novel nonlocal low-rank (NLR) based method for multitemporal synthetic aperture radar image CD. This method jointly exploits the powerful NLR-based despeckling and the effective cascade clustering. First, the NLR model is used to generate the difference image (DI), which consists of a patch grouping process and a low-rank minimizing process. Especially, the NLR minimization model contains a data fidelity term, which is based on the statistical distribution of speckle noise, and a regularization term, which uses the weighted nuclear norm. Then, the alternating direction methods of multipliers is introduced to solve this minimization problem. Second, after DI is generated, the principal component analysis is employed to extract the feature and a two-level clustering method is used to generate the final change map, which separates the intermediate class by using the neighbor information with Gaussian weighted distance. Experiment results demonstrate the effectiveness of the proposed method by comparing with some state-of-the-art methods.