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• W2896903403 abstract "In this paper, we present a novel inshore ship detection method based on convolutional neural network (CNN). Different from current inshore ship detection methods that need complex shape and texture analysis or sea and land segmentation, our method starts from a global search for the relatively distinct ship head with an efficient classification network. This can help to obtain the location of possible ship heads as well as the rough ship directions, which are beneficial to generate smaller and more precise candidate regions of ship targets. Compared with other region proposal methods, our method can produce a rather smaller set of proposals. Next, iterative bounding-box regression and classification are unified into a multitask network, which is constructed and trained specially by considering the practical condition of the inshore ships in remote sensing images. At last, nonmaximum suppression is applied to eliminate duplicate detections. Experiments on optical satellite images demonstrate the effectiveness and robustness of the proposed method for inshore ship detection." @default.
• W2896903403 created "2018-10-26" @default.
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• W2896903403 date "2018-11-01" @default.
• W2896903403 modified "2023-10-17" @default.
• W2896903403 title "Inshore Ship Detection Based on Convolutional Neural Network in Optical Satellite Images" @default.
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• W2896903403 doi "https://doi.org/10.1109/jstars.2018.2873190" @default.
• W2896903403 hasPublicationYear "2018" @default.
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