Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

Building extraction of remote sensing images is very important for urban planning. In the field of deep learning, in order to extract more detailed building features, more complex convolution operations and larger network models are usually used to segment buildings, resulting in low efficiency of automatic extraction. The existing network is difficult to balance the extraction accuracy and extraction speed. Considering the segmentation accuracy and speed, a Feature Residual Analysis Network (FRA-Net) is proposed to realize fast and accurate building extraction. The whole network includes two stages: encoding and decoding. In the encoding stage, a Separable Residual Module (SRM) is designed to extract feature information and extract building features from remote sensing images, avoiding the use of large convolution kernels to reduce the complexity of the model. In the decoding stage, the SRM is used for information decoding, and a multi-feature attention module is constructed to enhance the effective information. The experimental results on the LandCover dataset and Massachusetts Buildings dataset show that the reasoning speed has been greatly improved without reducing the segmentation accuracy.