Automatic extraction of surface water based on lightweight convolutional neural network

Abstract

Automatic extraction of surface water is of great significance to the study of the global water cycle and the dynamic management of water resources. At present, the water extraction accuracy of high-resolution multi-spectral remote sensing images has been greatly improved. But it is still affected by the shadow of the mountains and the shadow of the tall buildings inside the city. The spectral information of shadow is basically consistent with the spectral information of water, so the accuracy of any traditional index extraction of water will be questioned by users. Or the user must adjust threshold parameters many times to obtain good extraction results, which is contradictory to fast and large-area remote sensing monitoring. To solve the above problems, this paper firstly introduces the thermal infrared band at the data end for pre-treatment. Then, a lightweight neural network (EDCM) combining the most advanced lightweight image classification model and semantic segmentation model is proposed, which is specially used for fast, large area and automatic water extraction. Multi-scale training of samples using lightweight convolutional networks aims to extract multi-scale context information. The newly constructed model was tested in three highly heterogeneous scenarios, and the results showed that the trained EDCM model achieved the highest accuracy in all selected test areas, reaching more than 95.28%. The EDCM model can be used for the high-precision extraction of surface water in complex areas.

Keywords: Automatic; Deep learning; Global mapping; Lightweight convolutional neural network; Remote sensing; Water extraction.