doi:10.16186/j.cnki.1673-9787.2020070005

Received:2020/07/02

Revised:2020/09/22

Published:2022/07/15

Tailing pond extraction of Tangshan City based on Multi-Task-Branch Network

ZHANG Kunlun1, CHANG Yuguang2, PAN Jie1, LU Kaixuan1, ZAN Luyang1, CHEN Zhengchao1

1.Airborne Remote Sensing Center of Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;2.School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo  454000,Henan,China

Abstract: Aiming at the difficulty of accurate extraction of tailing pond location and boundary, an instance segmentation network with Multi-Task Branch structure based on the traditional instance segmentation network was proposed. Meanwhile, well-trained MTBNet was used to detect tailing ponds from GF-1 data for the Tangshan area. This novel method could achieve the detection accuracy of 78.8% while keeping the recall rate in a high level (95.8%). The method further optimized the qualities of the bounding box and mask, and enhanced the feature learning ability and effectively improved the instance segmentation accuracy of tailings pond, and it could be used as a data basis for dynamic monitoring of tailing ponds in Tangshan area， assisting tailing pond mining and reducing environmental pollution.

Key words:deep learning;GF-1 data;tailing pond instance segmentation;Tangshan area;MTBNet

基于MTBNet的唐山尾矿库提取_张昆仑.pdf