Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,w...Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training set.To solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection network.First,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model.Then,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features.Finally,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method.Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 61890962 and 61871179)the Scientific Research Project of Hunan Education Department (Grant No. 19B105)+3 种基金the Natural Science Foundation of Hunan Province (Grant Nos. 2019JJ50036 and 2020GK2038)the National Key Research and Development Project (Grant No. 2021YFA0715203)the Hunan Provincial Natural Science Foundation for Distinguished Young Scholars (Grant No. 2021JJ022)the Huxiang Young Talents Science and Technology Innovation Program (Grant No. 2020RC3013)
文摘Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training set.To solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection network.First,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model.Then,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features.Finally,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method.Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.
