Abstract
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.
Highlights
Lakes and rivers are the interactive connecting points of atmosphere, biosphere, lithosphere, and land hydrosphere [1]
Because the classical segmentation network (SegNet) performs upsampling during encoding stage, it lacks the fusion of high-level and low-level semantic information; as a result, detailed location information could be lost during the segmentation of water area remote sensing image
The biggest difference between the Cityscapes dataset and the water area segmentation dataset is that their objects are different, but using different objects for verification can better demonstrate the generalization performance of the networks proposed in this paper
Summary
Lakes and rivers are the interactive connecting points of atmosphere, biosphere, lithosphere, and land hydrosphere [1]. The neural network method can extract deep features of remote sensing images to achieve better classification [18], e.g., in water area segmentation. For remote sensing images, such as of lakes and rivers, which have high requirements for location and boundary extraction, the traditional CNN models are not accurate enough. To solve this problem, a semantic segmentation model which can achieve pixel-level classification has been proposed in recent years. A DNN normally has many convolution kernels, which increases the parameter numbers of the training network, and makes the training time-consuming and difficult [24] To solve these problems, a separable residual SegNet (SR-SegNet) is proposed in this paper.
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