Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy.
Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy.
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基于局部对比度方差加权信息熵的海洋SAR图像船舶检测
DOI：
10.3390/s18041196
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发表时间：
2018-04-13
期刊：
影响因子：
--
通讯作者：
Yang J
中科院分区：
文献类型：
--
作者：
Huo W;Huang Y;Pei J;Zhang Q;Gu Q;Yang J
合成孔径雷达（SAR）图像中的舰船目标检测是海上监视的关键问题之一。然而，由于海浪的多变性和海面的强回波，从异质和强杂波背景中检测船舶是非常困难的。本文提出了一种新的舰船检测方法，有效地区分复杂背景下的SAR图像中的舰船。首先，利用最大稳定极值区域（MSER）方法对输入SAR图像进行预筛选，以较低的计算复杂度得到舰船候选区域。然后，采用局部对比度方差加权信息熵（LCVWIE）来评价候选区域的复杂度以及候选区域与其邻域的相异性。最后，将候选区域的LCVWIE值与自适应阈值进行比较，以获得最终的检测结果。基于实测海洋SAR图像的实验结果表明，该方法在强杂波和非均匀背景下均能获得稳定的检测性能。同时，与现有的一些检测方法相比，它具有较低的计算复杂度。
Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, due to the varying ocean waves and the strong echo of the sea surface, it is very difficult to detect ships from heterogeneous and strong clutter backgrounds. In this paper, an innovative ship detection method is proposed to effectively distinguish the vessels from complex backgrounds from a SAR image. First, the input SAR image is pre-screened by the maximally-stable extremal region (MSER) method, which can obtain the ship candidate regions with low computational complexity. Then, the proposed local contrast variance weighted information entropy (LCVWIE) is adopted to evaluate the complexity of those candidate regions and the dissimilarity between the candidate regions with their neighborhoods. Finally, the LCVWIE values of the candidate regions are compared with an adaptive threshold to obtain the final detection result. Experimental results based on measured ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods.
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DOI：
10.1109/jstars.2012.2184271
发表时间：
2012-06-01
影响因子：
5.5
作者：
Migliaccio, Maurizio;Nunziata, Ferdinando;Paes, Rafael L.
通讯作者：
Paes, Rafael L.
用于 SAR 船舶检测的多层融合的基于上下文区域的卷积神经网络
影响因子：
5
作者：
Kang, Miao;Ji, Kefeng;Lin, Zhao
通讯作者：
Lin, Zhao
影响因子：
3.8
作者：
Bueger, Christian
通讯作者：
Bueger, Christian
影响因子：
4.8
作者：
Wang, Xiaolong;Chen, Cuixia
通讯作者：
Chen, Cuixia
影响因子：
2.3
作者：
Wang, Xiaolong;Chen, Cuixia
通讯作者：
Chen, Cuixia