Ship Detection and Fine-Grained Recognition in Large-Format Remote Sensing Images Based on Convolutional Neural Network

Jingrun Li, J. Tian, Peng Gao, Linfeng Li
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引用次数: 8

Abstract

Ship detection and fine-grained recognition in large-format remote sensing image are an important research direction in the field of remote sensing image detection. But less research has been done in this area. Aiming at this problem, this paper constructs a large-format remote sensing image ship target dataset with ship category information, and proposes a background filtering network and a ship fine-grained classification network. The background filtering network is used to quickly filter out the background area, and the ship fine-grained classification network is used to detect ship targets and distinguish ship categories. Compared with the previous method, the method proposed in this paper can significantly improve the efficiency of ship target detection in large-format remote sensing images, while also improving the detection accuracy.
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基于卷积神经网络的大幅面遥感图像船舶检测与细粒度识别
大幅面遥感图像中的船舶检测与细粒度识别是遥感图像检测领域的一个重要研究方向。但这方面的研究较少。针对这一问题,本文构建了包含船舶类别信息的大幅面遥感图像船舶目标数据集,并提出了背景滤波网络和船舶细粒度分类网络。背景滤波网络用于快速滤除背景区域,船舶细粒度分类网络用于检测船舶目标和区分船舶类别。与以往的方法相比,本文提出的方法可以显著提高大幅面遥感图像中舰船目标的检测效率,同时也提高了检测精度。
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