A new method for the extraction of tailing ponds from very high-resolution remotely sensed images:PSVED  

作　　者：Chengye Zhang Jianghe Xing Jun Li Shouhang Du Qiming Qin 

机构地区：[1]College of Geoscience and Surveying Engineering,China University of Mining and Technology,Beijing,People’s Republic of China [2]Key Laboratory of Coupling Process and Effect of Natural Resources Elements,Beijing,People’s Republic of China [3]Institute of Remote Sensing and Geographic Information System,School of Earth and Space Sciences,Peking University,Beijing,People’s Republic of China

出　　处：《International Journal of Digital Earth》2023年第1期2681-2703,共23页国际数字地球学报（英文）

基　　金：supported by the National Key Research and Development Program[grant number:2022YFF1303301];The Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements[grant number:2022KFKTC001];The National Natural Science Foundation of China[grant number:42271480];The Fundamental Research Funds for the Central Universities[grant number:2023ZKPYDC10,BBJ2023026].

摘　　要：Automatic extraction of tailing ponds from Very High-Resolution(VHR)remotely sensed images is vital for mineral resource management.This study proposes a Pseudo-Siamese Visual Geometry Group Encoder-Decoder network(PSVED)to achieve high accuracy tailing ponds extraction from VHR images.First,handcrafted feature(HCF)images are calculated from VHR images based on the index calculation algorithm,highlighting the tailing ponds'signals.Second,considering the information gap between VHR images and HCF images,the Pseudo-Siamese Visual Geometry Group(Pseudo-Siamese VGG)is utilized to extract independent and representative deep semantic features from VHR images and HCF images,respectively.Third,the deep supervision mechanism is attached to handle the optimization problem of gradients vanishing or exploding.A self-made tailing ponds extraction dataset(TPSet)produced with the Gaofen-6 images of part of Hebei province,China,was employed to conduct experiments.The results show that the proposed'method_achieves the best visual performance and accuracy for tailing ponds extraction in all the tested methods,whereas the running time of the proposed method maintains at the same level as other methods.This study has practical significance in automatically extracting tailing ponds from VHR images which is beneficial to tailing ponds management and monitoring.

关 键 词：Semantic segmentation tailing storage facilities Pseudo-Siamese network VHR images deep supervision mechanism 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]