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

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 a...

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Autores principales:	Yueming Duan, Wenyi Zhang, Peng Huang, Guojin He, Hongxiang Guo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning convolutional neural network land surface water extraction GaoFen-1D attention mechanism multi-scale Science Q
Acceso en línea:	https://doaj.org/article/6dedcaa001304bcf8375df7dc5d19a6c
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spelling	oai:doaj.org-article:6dedcaa001304bcf8375df7dc5d19a6c2021-11-25T18:54:29ZA New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images10.3390/rs132245762072-4292https://doaj.org/article/6dedcaa001304bcf8375df7dc5d19a6c2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4576https://doaj.org/toc/2072-4292Mapping 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.Yueming DuanWenyi ZhangPeng HuangGuojin HeHongxiang GuoMDPI AGarticledeep learningconvolutional neural networkland surface water extractionGaoFen-1Dattention mechanismmulti-scaleScienceQENRemote Sensing, Vol 13, Iss 4576, p 4576 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning convolutional neural network land surface water extraction GaoFen-1D attention mechanism multi-scale Science Q
spellingShingle	deep learning convolutional neural network land surface water extraction GaoFen-1D attention mechanism multi-scale Science Q Yueming Duan Wenyi Zhang Peng Huang Guojin He Hongxiang Guo A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images
description	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.
format	article
author	Yueming Duan Wenyi Zhang Peng Huang Guojin He Hongxiang Guo
author_facet	Yueming Duan Wenyi Zhang Peng Huang Guojin He Hongxiang Guo
author_sort	Yueming Duan
title	A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images
title_short	A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images
title_full	A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images
title_fullStr	A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images
title_full_unstemmed	A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images
title_sort	new lightweight convolutional neural network for multi-scale land surface water extraction from gaofen-1d satellite images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6dedcaa001304bcf8375df7dc5d19a6c
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A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images

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