Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor
Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor
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DOI：
10.3390/rs10030400
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发表时间：
2018-03-01
期刊：
影响因子：
5
通讯作者：
Xu, Fang
中科院分区：
文献类型：
--
作者：
Dong, Chao;Liu, Jinghong;Xu, Fang
光学遥感(ORS)图像中舰船自动检测的主要挑战包括云、浪、岛、尾迹杂波，甚至目标的高度可变性。针对这些问题，本文提出了一种实用的船舶检测方案。该方案包括两个主要的从粗到精的阶段：预选和甄别。在预筛选阶段，根据感兴趣区域(ROI)的高度非均匀区域与均匀背景的统计特征的差异，构造了一种新的视觉显著检测方法。它可以作为定位候选区域的指南。这样，不仅可以准确地检测到目标，而且还可以显著减少虚警。在识别阶段，为了更好地表示目标，提取了表征舰船目标的形状和纹理特征，并将其连接为特征向量，用于后续的分类。此外，组合特征对于旋转是不变的。最后，使用可训练的高斯支持向量机(SVM)分类器来验证真实的舰船候选。通过与已有工作的详细比较，我们证明了所提出的分层检测方法的优越性能。
Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main coarse-to-fine stages: prescreening and discrimination. In the prescreening stage, we construct a novel visual saliency detection method according to the difference of statistical characteristics between highly non-uniform regions which allude to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions. In this way, not only can the targets be precisely detected, but false alarms are also significantly reduced. In the discrimination stage, to get a better representation of the target, both shape and texture features characterizing the ship target are extracted and concatenated as a feature vector for subsequent classification. Moreover, the combined feature is invariant to the rotation. Finally, a trainable Gaussian support vector machine (SVM) classifier is performed to validate real ships out of ship candidates. We demonstrate the superior performance of the proposed hierarchical detection method with detailed comparisons to existing efforts.