Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images |
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| Authors: | Kai Du Yi Ma Zongchen Jiang Xiaoqing Lu Junfang Yang |
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| Affiliation: | College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China;First Institute of Oceanology,Ministry of Natural Resources,Qingdao 266061,China;First Institute of Oceanology,Ministry of Natural Resources,Qingdao 266061,China;Technology Innovation Center for Ocean Telemetry,Ministry of Natural Resources,Qingdao 266061,China;National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology,Xi'an 710072,China;School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150001,China;National Satellite Ocean Application Service,Beijing 100081,China;College of Oceanography and Space Informatics,China University of Petroleum(East China),Qingdao 266580, China |
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| Abstract: | Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents. Multispectral images have the advantages of high spatial resolution, short revisit period, and wide imaging width, which is suitable for large-scale oil spill monitoring. However, in wide remote sensing images, the number of oil spill samples is generally far less than that of seawater samples. Moreover, the sea surface state tends to be heterogeneous over a large area, which makes the identification of oil spills more difficult because of various sea conditions and sunglint. To address this problem, we used the F-Score as a measure of the distance between forecast value and true value, proposed the Class-Balanced F loss function (CBF loss function) that comprehensively considers the precision and recall, and rebalances the loss according to the actual sample numbers of various classes. Using the CBF loss function, we constructed convolution neural networks (CBF-CNN) for oil spill detection. Based on the image acquired by the Coastal Zone Imager (CZI) of the Haiyang-1C (HY-1C) satellite in the Andaman Sea (study area 1), we carried out parameter adjustment experiments. In contrast to experiments of different loss functions, the F1-Score of the detection result of oil emulsions is 0.87, which is 0.03–0.07 higher than cross-entropy, hinge, and focal loss functions, and the F1-Score of the detection result of oil slicks is 0.94, which is 0.01–0.09 higher than those three loss functions. In comparison with the experiment of different methods, the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks, supports vector machine and random forests models, and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods. To verify the applicability of the CBF-CNN model in different observation scenes, we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments, which include two studies areas (study area 2 and study area 3). The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88, which is 0.16–0.24 higher than that of other methods, and the F1-Score of the detection result of oil slicks is 0.96–0.97, which is 0.06–0.23 higher than that of other methods. Based on all the above experiments, we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills, and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images. |
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| Keywords: | oil spills CNN classification loss function sunglint detection |
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