计算机科学
稳健性(进化)
遥感
人工智能
像素
卷积神经网络
特征(语言学)
深度学习
特征提取
计算机视觉
频道(广播)
模式识别(心理学)
地质学
基因
哲学
生物化学
语言学
化学
计算机网络
作者
Qingpeng Li,Lichao Mou,Qingjie Liu,Yunlong Wang,Xiao Xiang Zhu
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2018-12-01
卷期号:56 (12): 7147-7161
被引量:145
标识
DOI:10.1109/tgrs.2018.2848901
摘要
Ship detection is an important and challenging task in remote sensing applications. Most methods utilize specially designed hand-crafted features to detect ships, and they usually work well only on one scale, which lack generalization and impractical to identify ships with various scales from multiresolution images. In this paper, we propose a novel deep feature-based method to detect ships in very high-resolution optical remote sensing images. In our method, a regional proposal network is used to generate ship candidates from feature maps produced by a deep convolutional neural network. To efficiently detect ships with various scales, a hierarchical selective filtering layer is proposed to map features in different scales to the same scale space. The proposed method is an end-to-end network that can detect both inshore and offshore ships ranging from dozens of pixels to thousands. We test our network on a large ship data set which will be released in the future, consisting of Google Earth images, GaoFen-2 images, and unmanned aerial vehicle data. Experiments demonstrate high precision and robustness of our method. Further experiments on aerial images show its good generalization to unseen scenes.
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