A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images

Yueming Duan,Wenyi Zhang,Hongxiang Guo,Peng Huang,Guojin He

doi:10.3390/rs13224576

Abstract

Mapping land surface water automatically and accurately is closely related to human activity, biological reproduction, and the ecological environment. High spatial resolution remote sensing image (HSRRSI) data provide extensive details for land surface water and gives reliable data support for the accurate extraction of land surface water information. The convolutional neural network (CNN), widely applied in semantic segmentation, provides an automatic extraction method in land surface water information. This paper proposes a new lightweight CNN named Lightweight Multi-Scale Land Surface Water Extraction Network (LMSWENet) to extract the land surface water information based on GaoFen-1D satellite data of Wuhan, Hubei Province, China. To verify the superiority of LMSWENet, we compared the efficiency and water extraction accuracy with four mainstream CNNs (DeeplabV3+, FCN, PSPNet, and UNet) using quantitative comparison and visual comparison. Furthermore, we used LMSWENet to extract land surface water information of Wuhan on a large scale and produced the land surface water map of Wuhan for 2020 (LSWMWH-2020) with 2m spatial resolution. Random and equidistant validation points verified the mapping accuracy of LSWMWH-2020. The results are summarized as follows: (1) Compared with the other four CNNs, LMSWENet has a lightweight structure, significantly reducing the algorithm complexity and training time. (2) LMSWENet has a good performance in extracting various types of water bodies and suppressing noises because it introduces channel and spatial attention mechanisms and combines features from multiple scales. The result of land surface water extraction demonstrates that the performance of LMSWENet exceeds that of the other four CNNs. (3) LMSWENet can meet the requirement of high-precision mapping on a large scale. LSWMWH-2020 can clearly show the significant lakes, river networks, and small ponds in Wuhan with high mapping accuracy.

Highlights

The results show that the random forest classifier is better than the threshold algorithms (NDVI, normalized difference water index (NDWI), and modified normalized difference water index (MNDWI)) and the other machine learning methods for information extraction in land surface water
This paper gives an improved lightweight convolutional neural network (CNN) named LMSWENet for land surface water information extraction and mapping in Wuhan based on GaoFen-1D high-resolution remote sensing images
Four CNNs (DeeplabV3+, fully convolutional network (FCN), PSPNet, and UNet) for semantic segmentation are employed for comparison

Summary

Introduction

Land surface water plays a significant role in land cover changes, environmental changes, and climate changes in many parts of the world. The health, ecological, economic, and social effects of water changes have become a popular subject of academic study in recent years [1,2,3,4,5,6,7]. Using satellite remote sensing images to extract the information of land surface water, such as water position, area, shape, and river width, has become an effective way to obtain land surface water information rapidly [8]. With the development of aerospace technology, the spatial resolution of remote sensing images increases significantly, Remote Sens.

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Discussion

Conclusion