Food safety supervision mechanism is a strong guarantee to promote the smooth implementation of China's food safety laws and regulations,and it is implemented through legal,administrative,economic,moral and other ...Food safety supervision mechanism is a strong guarantee to promote the smooth implementation of China's food safety laws and regulations,and it is implemented through legal,administrative,economic,moral and other integrated policy instruments,as well as media publicity,quality traceability,network tracking,information disclosure and other non-administrative means. Along with strengthening supervision and control means,the people's food safety in China is safeguarded,and the healthy development of the food industry is promoted.展开更多
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(PS...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.展开更多
基金Supported by Liaoning Science Public Welfare Research Fund(20170046)
文摘Food safety supervision mechanism is a strong guarantee to promote the smooth implementation of China's food safety laws and regulations,and it is implemented through legal,administrative,economic,moral and other integrated policy instruments,as well as media publicity,quality traceability,network tracking,information disclosure and other non-administrative means. Along with strengthening supervision and control means,the people's food safety in China is safeguarded,and the healthy development of the food industry is promoted.
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]+1 种基金The National Natural Science Foundation of China[grant number:42271480]The Fundamental Research Funds for the Central Universities[grant number:2023ZKPYDC10,BBJ2023026].
文摘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.
