Automatic mapping of gully from satellite images using asymmetric non-local LinkNet: A case study in Northeast China  

作　　者：Panpan Zhu Hao Xu Ligang Zhou Peixin Yu Liqiang Zhang Suhong Liu 

机构地区：[1]College of Computer Sciences and Technology,Chongqing University of Posts and Telecommunications,Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology,Chongqing,400065,China [2]College of Environment and Ecology,Chongqing University,Chongqing,400044,China [3]Chongqing Municipal Ecological and Environmental Monitoring Center,Chongqing,401120,China [4]Beijing Key Laboratory of Environmental Remote Sensing and Digital Cities,Faculty of Geographical Science,Beijing Normal University,Beijing,100875,China [5]China National Environmental Monitoring Centre,Beijing,100012,China

出　　处：《International Soil and Water Conservation Research》2024年第2期365-378,共14页国际水土保持研究（英文）

基　　金：supported by the National Natural Science Foundation of China(42201419).

摘　　要：Gully erosion can lead to the destruction of farmland and the reduction in crop yield.Gully mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control measures.The narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images challenging.To implement gully erosion mapping,we developed a small training samples-oriented lightweight deep leaning model,called asymmetric non-local LinkNet(ASNL-LinkNet).The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted features.Experiment results show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning methods.The quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to 0.72.This study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.

关 键 词：Gully erosion mapping High-resolution satellite images Deep learning Black soil 

分 类 号：P623[天文地球—地质矿产勘探]