Abstract
Lake water body extraction from remote sensing images is a key technique for spatial geographic analysis. It plays an important role in the prevention of natural disasters, resource utilization, and water quality monitoring. Inspired by the recent years of research in computer vision on fully convolutional neural networks (FCN), an end-to-end trainable model named the multi-scale lake water extraction network (MSLWENet) is proposed. We use ResNet-101 with depthwise separable convolution as an encoder to obtain the high-level feature information of the input image and design a multi-scale densely connected module to expand the receptive field of feature points by different dilation rates without increasing the computation. In the decoder, the residual convolution is used to abstract the features and fuse the features at different levels, which can obtain the final lake water body extraction map. Through visual interpretation of the experimental results and the calculation of the evaluation indicators, we can see that our model extracts the water bodies of small lakes well and solves the problem of large intra-class variance and small inter-class variance in the lakes’ water bodies. The overall accuracy of our model is up to 98.53% based on the evaluation indicators. Experimental results demonstrate that the MSLWENet, which benefits from the convolutional neural network, is an excellent lake water body extraction network.
Highlights
Lake water body extraction is a fundamental and important field of remote sensing image analysis
mean intersection over union (MIoU) is the average of the ratio of the number of correctly classified pixels to the number of ground reference pixels and the number of pixels detected in the corresponding category
Our proposed method named MSLWENet achieves state-of-the-art performance on the lake dataset than DeepLab V3+, PSPNet, MWEN, and Unet
Summary
Lake water body extraction is a fundamental and important field of remote sensing image analysis. There are approximately 304 million natural lakes on the surface of the Earth, which are composed by millions of small water bodies, covering about 4.6 million kilometers of water [1]. Lakes have the function of developing irrigation [3], providing a source of drinking water on which human life depends [4], and transportation [5]. Remote sensing images of lakes contain a great deal of information that can be used in other areas, such as disaster monitoring, the development of agriculture, livestock farming, and geographic planning. It is important to study the automatic lake water body extraction from remote sensing images
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