发布时间: 2018-09-16 摘要点击次数: 全文下载次数: DOI: 10.11834/jig.170612 2018 | Volume 23 | Number 9 遥感图像处理

1. 华中科技大学自动化学院, 武汉 430074;
2. 多谱信息处理技术国家级重点实验室, 武汉 430074;
3. 北京航天自动控制研究所, 北京 100854
 收稿日期: 2017-12-01; 修回日期: 2018-02-08 基金项目: 国家十三五科技预研基金项目（41415020402） 第一作者简介: 张磊, 1989年生, 男, 博士研究生, 主要从事实时自动目标识别、嵌入式图像处理系统、计算机视觉方面的研究。E-mail:lzhang89@163.com;洪星, 男, 硕士研究生, 主要从事实时自动目标识别方法、计算机视觉方面的研究。E-mail:hust_hongxing@126.com;周斌, 男, 高级工程师, 主要研究方向为精确制导。E-mail:giggsnet@163.com. 中图法分类号: TP391.41 文献标识码: A 文章编号: 1006-8961(2018)09-1424-09

# 关键词

Inshore ship detection in high-resolution remote sensing image using projection analysis
Zhang Lei1, Hong Xing1, Wang Yuehuan1,2, Zhou Bin3
1. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;
2. National Key Lab of Science and Technology on Muti-spectral Information Processing, Wuhan 430074, China;
3. Beijing Aerospace Automatic Control Institute, Beijing 100854, China
Supported by: Pre-research Foundation of the Thirteenth Five-year Plan for National Science and Technology of China(41415020402)

# Abstract

Objective 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 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 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 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.

# Key words

inshore ship detection; dock shoreline detection; projection analysis; high resolution remote sensing image; side-by-side ships detection

# 1.2.1 基于改进的Hough变换的直线检测

Hough变换一种从图像空间到参数空间的映射关系，常用于图像中特定几何形状的检测。对于直线的检测问题而言，任意一条直线都可以用参数$\rho $$\theta 完全确定下来，其中\rho 确定了该直线到原点的距离，\theta 确定了该直线的方向。其函数方程可表示为  f\left( {\rho ,\theta } \right) = \rho - x{\text{cos}}\left( \theta \right) - y{\text{sin}}\left( \theta \right) = 0 标准的Hough变换需要遍历全图像素，对每一个像素点再遍历所有的角度\theta ，往往在计算上开销较大。针对这一问题，提出一种基于梯度信息的Hough变换方法。首先，只对分割后的边缘点位置检测，大大减少了遍历点的数量。此外，直线上的边缘点梯度方向与该直线方向理论上垂直，因此边缘点的梯度方向可以作为该点Hough角度的指导，考虑到梯度方向可能存在误差，设置60°的角度阈值，减少角度遍历范围，缩短了计算时间；同时，梯度值越大的边缘点更有可能处于水域边界，因此在Hough投票环节，本文将Hough空间的累加器每次加1改为加上一个由梯度强度确定的权值。详细过程如下： 1) 构造双精度累加器数组\mathit{\boldsymbol{H}}\left[ {2 \times d \times 180} \right]，其中d表示图像对角线长度，直线参数\rho 取值范围在[-d, d]区间内； 2) 对于边缘图像上的每个边缘点，其坐标为(i, j)，其梯度图像对应的梯度强度为g，梯度方向为\gamma 3) 直线方向参数\theta$$\gamma$+60°到$\gamma$+120°递增，步长为1°，$\rho = i \times {\text{cos}}\left(\theta \right) + j \times {\text{sin}}\left(\theta \right)$

# 2.2 参数敏感性分析

Hough变换选取峰值数目和投影空间范围阈值属于不敏感参数。Hough变换选取峰值数目决定了提取直线的数目，在实验中将这一峰值数目设置为50200之间，这是考虑到在绝大多数情形下，这一数目是大于图像中港口岸线数的。由于提取的直线会根据先验知识进一步去除干扰直线，因此Hough变换选取峰值的数目设定范围可以较为宽松。同理，由于投影分析时会去除投影基准值影响，投影空间范围阈值也可根据实际情况适当放宽。

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