Ship Detection in Spaceborne Optical Image With SVD Networks
Ship Detection in Spaceborne Optical Image With SVD Networks
复制标题
使用 SVD 网络进行星载光学图像中的船舶检测
DOI：
10.1109/tgrs.2016.2572736
复制
发表时间：
2016-10-01
影响因子：
8.2
通讯作者：
Shi, Zhenwei
中科院分区：
文献类型：
--
作者：
Zou, Zhengxia;Shi, Zhenwei
基于星载光学图像的船舶自动检测是一个具有挑战性的课题，由于其在海上安全和交通管制中的广泛应用前景而受到广泛关注。虽然近年来提出了一些光学图像船舶检测方法，但在这项任务中仍然存在三个障碍：1）云和强波浪的推断; 2）难以同时检测近岸和近海船舶; 3）计算费用高。在本文中，我们提出了一种新的船舶检测方法称为SVD网络（SVDNet），这是快速，鲁棒性，结构紧凑。SVDNet是基于最近流行的卷积神经网络和奇异值补偿算法设计的。它提供了一种简单而有效的方法来自适应地从遥感图像中学习特征。最后，利用高分一号和委内瑞拉遥感卫星的部分星载光学图像对该方法进行了验证。实验结果表明，我们的方法实现了高的检测鲁棒性和一个理想的时间性能，在所有上述三个问题。
Automatic ship detection on spaceborne optical images is a challenging task, which has attracted wide attention due to its extensive potential applications in maritime security and traffic control. Although some optical image ship detection methods have been proposed in recent years, there are still three obstacles in this task: 1) the inference of clouds and strong waves; 2) difficulties in detecting both inshore and offshore ships; and 3) high computational expenses. In this paper, we propose a novel ship detection method called SVD Networks (SVDNet), which is fast, robust, and structurally compact. SVDNet is designed based on the recent popular convolutional neural networks and the singular value decompensation algorithm. It provides a simple but efficient way to adaptively learn features from remote sensing images. We evaluate our method on some spaceborne optical images of GaoFen-1 and Venezuelan Remote Sensing Satellites. The experimental results demonstrate that our method achieves high detection robustness and a desirable time performance in response to all of the above three problems.