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• W4297094920 abstract "Smoke is more observable than open fires. Optical satellite video has the advantages of a wide monitoring range, fast response speed, and good economy in large-scale surface smoke monitoring tasks. It can be used in wide-area forest wildfire monitoring, battlefield dynamic monitoring, disaster relief decision-making. The smoke segmentation method based on traditional handcrafted features is easily limited by the scene and data. This paper introduces the deep learning method to the optical satellite video smoke target segmentation. However, due to the lack of real smoke images and the blurred edges of smoke, there are currently few labeled datasets for smoke segmentation in high-resolution optical satellite imagery scenes, which cannot provide sufficient training data for deep learning models. The smoke image from the satellite perspective also has the characteristics of multi-scale features and ground object background interference. To solve the above problems, we construct a set of high-resolution optical satellite imagery smoke synthesis datasets based on the optical imaging process of smoke targets, which saves the cost of manual labeling. In addition, we design an attention-guided optical satellite video smoke segmentation network model (AOSVSSNet), which can effectively suppress the ground object background's false alarm and extract the smoke's multi-scale features. Synthetic data faces the transferability problem in real-world applications, so the physical constraints of the smoke imaging process are introduced into the loss function to improve the generalization of the model in real smoke data. The comprehensive evaluation results show that the method outperforms representative semantic segmentation networks." @default.
• W4297094920 created "2022-09-27" @default.
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• W4297094920 date "2022-01-01" @default.
• W4297094920 modified "2023-09-25" @default.
• W4297094920 title "AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network" @default.
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• W4297094920 doi "https://doi.org/10.1109/jstars.2022.3209541" @default.
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