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收稿：2017-12-01，
修回：2018-2-8，
纸质出版：2018-09-16
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目的
2
高分辨率遥感图像中，靠岸舰船检测有着广泛的应用前景，其主要难点在于舰船与港口陆地在空间上紧邻，在颜色和纹理特征上相似，舰船与港口陆地难以分割。针对这种情况，利用港口岸线平直的几何特点和靠岸舰船多为舷靠的停泊特点，提出一种基于投影分析的靠岸舰船检测方法。
方法
2
首先，对原始图像进行预处理，利用K-means聚类算法与区域生长算法相结合的方式得到海陆分割图像，利用Sobel算子与Otsu分割结合的方式获取边缘图像；然后，通过改进的Hough变换提取直线特征，结合港岸几何特性定位港口岸线；再将海陆分割后的二值图像向沿岸线和垂直岸线两个方向进行投影，根据沿岸线方向投影形态确定和分离并靠舰船，根据垂直岸线方向的投影形态定位舰船目标；最后，利用舰船尺寸、长宽比、最小外接矩形占空比特征去除虚警。
结果
2
在15个港口场景不同分辨率的遥感图像测试集上，本文方法整体检测率达到85.4%，虚警率达17.2%；限定分辨率范围在2~4 m的情形下，检测率提高到93.5%，虚警率降低至5.3%。
结论
2
本文方法简单有效，无需港口先验信息，适用于多尺度和多方向的靠岸舰船目标检测任务，对不同类型舰船形态差异具有鲁棒性，且能够分离并靠舰船。
Objective
2
In high-resolution remote sensing images
inshore ship detection has broad application prospects
such as ocean surveillance
fisheries management
and military reconnaissance. However
unlike the ship detection under pure sea background
inshore ship detection is much more challenging
considering the complex background of the port. The main difficulty of inshore ship detection is that the ship and the dock are adjacent in space and similar in color and texture features
thereby introducing difficulties in distinguishing them. Effective methods for this task are scarce. The existing methods can be mainly into three types. The first is based on template matching but prior geographical information of the port is needed
which is often difficult to obtain. The second type is based on the ship contour method
in which the robustness is low and detecting side-by-side ships is difficult. The third type is based on local features of the ship
which often assume that the ship has a V-shaped bow and is powerless to other ships. Other than the existing methods
a method for inshore ship detection using projection analysis is proposed in this paper. Our method is based on the observation that the shoreline of a dock is typically straight and inshore ships are usually anchored along the ship's rail.
Method
2
First
the original image is preprocessed by two sibling approaches:one that segments the sea and land and another that extracts edges. For sea-land segmentation
the K-means clustering algorithm and region-growing algorithm are combined to improve the segmentation quality
which is significant for our method. Meanwhile
the original image is processed to a gradient image by Sobel operator
and then the gradient image is segmented to an edge image with the Otsu algorithm. Second
an improved Hough transform is conducted on the edge image to extract the straight lines
among which are dock shorelines. To remove interference lines
we assume that all extracted lines of dock shorelines should only lie on the border of water. Then
we start to search for ships on both sides of the located dock shorelines. Taking one dock shoreline as an example
we project the sea-land segmentation image perpendicular to the dock shoreline direction and obtain a projection curve. If a ship is anchored along the dock shoreline
the projection curve shape is convex. Otherwise
the curve shape is flat. Furthermore
we can locate the ship with a bounding box by analyzing the curve shape conveniently. To separate side-by-side ships
we conduct another projection in the dock shoreline direction and separate these ships by analyzing the peak and valley of the projection curve. Finally
we remove false alarms using features of ship size
aspect ratio
and duty ratio.
Result
2
We have randomly chosen 292 high-resolution remote sensing images of 15 different scenes on Google Earth to test our method. The test images have a total of 962 ships
comprising 139 aircraft carriers
794 destroyers
and 29 civilian ships. The resolution of the images ranges from 1 m to 5.5 m. Thus
the ships on these images are variant in scale
orientation
and even brightness. Our method has correctly detected 822 of the 962 ships
comprising 134 aircraft carriers
666 destroyers
and 22 civilian ships. These results represent 85.4% of the total detection rate
96.4% of the aircraft carrier detection rate
and 83.9% destroyer detection rate. Meanwhile
we have 171 false alarm targets
thereby representing a false alarm rate of 17.2%. Results show that if the resolution is limited from 2 m to 4 m
the total detection rate grows up to 93.5% and the false alarm rate decreases to 5.3%. However
our method is sensitive to the quality of sea-land segmentation
which is essential to extracting the straight line features of the dock shorelines. Thus
the detection rate is compromised on very complex backgrounds
as shown in this paper.
Conclusion
2
Our method is simple and effective for inshore ship detection tasks. No prior information on the harbors is needed. The method is suitable for detection of inshore ships in variable resolutions and directions. It is robust to ship shapes and side-by-side ships can be detected as well. Given that our method is sensitive to the quality of sea-land segmentation
more powerful segmentation algorithms
which is our future research direction
may be effective.
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