目的 在光学遥感图像中,针对舷靠舰船灰度和纹理特征与港口相近,传统方法检测效果不理想的问题,提出一种基于局部显著特征的舷靠舰船检测方法。方法 首先,对原始图像预处理得到海陆分割后的二值图像;然后,提取二值图像中的直线段作为局部显著特征检测舰船目标;再将直线段提取结果与舰首检测相结合,建立舷靠舰船检测模型;最后,通过计算舰船几何尺寸及环境信息分析确定舰船目标。结果 在两幅不同场景的光学遥感图像中验证本文方法并与其他算法进行对比,本文方法识别率可达100%,且不存在误检和漏检情况,相比于其他算法具有一定优势。在舰船背景复杂或停泊朝向不定时,文中方法可有效判别舰船停靠方向并对舰船目标进行正确标记。结论 在复杂背景环境及其他干扰下,应用本文方法检测舷靠舰船目标准确率高,鲁棒性强,具有较高适应性。

Objective Automatic inshore ship detection from remote sensing imagery has many important applications, such as ship change detection and harbor dynamic surveillance. Stable performance of inshore ship detection is vital to the analysis of ship change and the determination of the harbor surveillance effect. Ship detection using optical remote sensing images has been a hot research topic. However, detecting inshore ships utilizing the traditional area-based method is difficult because the gray scale and texture character of inshore ships are similar to that of the shore. Therefore, we propose a method of inshore ship detection using local salient characteristics. Method First, the binary image is obtained by water and land segmentation preprocessing. Then, the line segments from binary images are extracted as the local salient features to detect ship targets. Next, line segment extraction result is combined with ship bow detection result to generate the ship detection model. Finally, the ship targets are acquired by calculating the ship geometric size and analyzing the environmental information. Result Experimental results indicate that the proposed inshore ship detection method is more effective and can robustly adapt to the complex background and mooring orientation. The detection result is more accurate compared with that of traditional methods, and the relognition rate is 100%.Conclusion In the complex background environment and other interferences, this method exhibits a high recognition rate, with high robustness and high adaptability.