摘要 星/机载光学遥感图像视场广阔、场景复杂,且受岸边建筑、碎云影响易产生大量与舰船目标高相似虚警,给舰船检测带来极大干扰,传统海洋舰船检测算法难以有效提取利于检测的鉴别性特征,导致舰船检测率低、虚警率高。鉴于此,本文从低虚警、低漏检角度,提出一种结合层次化搜索与视觉残差网络的光学舰船目标检测方法。首先基于纹理积分图分割出海陆区域;其次,结合多尺度局部结构特征提取目标候选区域;然后,通过基于多维度视觉特征的层次化策略进行初级虚警剔除;最后,基于视觉残差网络对疑似候选区进行精细化虚警剔除,得到最终检测结果。基于GF2光学遥感数据对本文所提算法进行测试验证,本文算法综合检测率92.0%,虚警率12.58%,平均处理时间0.5s,检测效果好、效率高,对各种场景的适应性好,可实现复杂环境光学舰船的准确、高效检测定位。 The star/airborne optical remote sensing image has a wide field of view and a complex scene.It is easy to produce a large number of false alarms that are similar to the ship’s target due to the impact of the shore construction and broken cloud,causing great interference to the ship’s detection.Traditional marine ship detection algorithms are difficult to be effective extracting discriminative features that are conducive to detection,results in low detection rates and high false alarm rates for ships.In view of this,this paper proposes an optical ship target detection method combining hierarchical search and visual residual network from the perspective of low false alarm and low missed detection.Firstly,the land and sea area are segmented based on the texture integral map;secondly,the target candidate area is extracted by combining the multi-scale local structural features;then,the primary false alarm is removed by the layered removal strategy based on multi-dimensional visual features;finally,the visual residuals are built the network finely removes false alarms from suspected candidate areas to obtain the final detection result.based on the GF2 remote sensing GF2 set,the algorithm proposed in this paper is tested and verified.The comprehensive detection rate of this algorithm is 92.0%,the false alarm rate is 12.58%,the average processing time is 0.5 s,the detection effect is good,the efficiency is high,and the adaptability to various scenes is good.It can achieve accurate and efficient detection and positioning of optical ships in complex environments.
机构地区 北京跟踪与通信技术研究所 北京市遥感信息研究所
出处 《光电工程》 CAS CSCD 北大核心 2021年第4期36-43,共8页 Opto-Electronic Engineering
关键词 光学舰船 纹理积分图 层次化虚警剔除 视觉残差网络 optical ship texture integral map hierarchical false alarm rejection visual residual network
