Remote sensing images contain complex feature information, and traditional convolutional networks cannot effectively model the contextual relationships. To address this problem, we propose a semantic segmentation network for remote sensing images based on semantic relationship aware. We construct the semantic relationship aware module to obtain the global semantic information of remote sensing images by self-attention. In addition, the separable space convergence pyramid module was constructed to effectively utilize the feature information in the high-level feature maps. By separable convolution with different dilation rates, the network can acquire multiscale semantic information. Our semantic relation aware network (SRANet) improves the overall accuracy by 0.33% over the benchmark network in the Vaihingen dataset and by 0.42% in the Potsdam dataset. The class activation maps show that the SRANet has ideal activation responses for targets at different scales in images. Furthermore, our SRANet can produce competitive segmentation performance compared with other state-of-the-art segmentation networks.
Due to the small number of remote sensing image datasets, it is difficult to train deep neural networks, so we first constructed a two-branch network based on exponentially learning multi-labeled remote sensing image features. In addition, most multi-labeled remote sensing image classification networks use ResNet as the backbone network, which ignores inter-channel correlation, so we used SE-ResNet as the two-branch backbone network. Finally, since most traditional methods focus only on the visual elements in an image or only on the dependencies between multi-labels, we combined the two and constructed a multi-label remote sensing image classification network, Dual-branching Channel Attention and Graph Convolution Network (DCA-GCN), based on a two-branch network and graph convolution, using the two-branch channel attention structure to extract richer image features from remote sensing images and the graph convolution network to establish the dependencies between multi-labels. DCA-GCN achieves relatively excellent results on three publicly available multi-label remote sensing datasets.
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