用于遥感舰船细粒度检测与识别的关键子区域融合网络  

Key sub-region feature fusion network for fine-grained ship detection and recognition in remote sensing images

作　　者：张磊[1] 陈文[1] 王岳环[1] Zhang Lei;Chen Wen;Wang Yuehuan(National Key Laboratory of Science and Technology on Multi-spectral Information Processing,School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学人工智能与自动化学院多谱信息处理国家重点实验室,武汉430074

出　　处：《中国图象图形学报》2023年第9期2940-2955,共16页Journal of Image and Graphics

基　　金：国家十三五科技预研基金项目(41415020402)。

摘　　要：目的遥感图像中的舰船目标细粒度检测与识别在港口海域监视以及情报搜集等应用中有很高的实际应用价值,但遥感图像中不同种类的舰船目标整体颜色、形状与纹理特征相近,分辨力不足,导致舰船细粒度识别困难。针对该问题,提出了一种端到端的基于关键子区域特征的舰船细粒度检测与识别方法。方法为了获得更适于目标细粒度识别的特征,提出多层次特征融合识别网络,按照整体、局部子区域两个层次从检测网络得到的候选目标区域中提取特征。然后结合候选目标中所有子区域的信息计算每个子区域的判别性显著度,对含有判别性组件的关键子区域进行挖掘。最后基于判别性显著度将子区域特征与整体特征进行自适应融合,形成表征能力更强的特征,对舰船目标进行细粒度识别。整个检测与识别网络采用端到端一体化设计,所有候选目标特征提取过程只需要经过一次骨干网络的计算,提高了计算效率。结果在公开的带有细粒度类别标签的HRSC2016(high resolution ship collection)数据集L3任务上,本文方法平均准确率为77.3%,相较于不采用多层次特征融合识别网络提升了6.3%;在自建的包含45类舰船目标的FGSAID(fine-grained ships in aerial images dataset)数据集上,本文方法平均准确率为71.5%。结论本文方法有效挖掘并融合了含有判别性组件的子区域的特征,解决了目标整体特征分辨力不足导致的细粒度目标识别困难问题,相较于现有的遥感图像舰船目标检测与识别算法准确性有明显提升。Objective The ocean has great economic and military value.The development of human society increases the impact of ocean activities on the development of a country.The sea is an important carrier of marine activities.Thus,the recognition and monitoring of ship targets in key sea areas through remote sensing images are crucial to the national defense and development of the economy.Fine-grained ship detection and recognition in high-resolution remote sensing images refer to the identification of specific types of ships based on ship detection.A precise and detailed classification is valuable in practical application fields,such as sea surveillance and intelligence gathering.Instead of coarse-grained classification categories,such as warcraft and merchant ships,specific ship types,such as Arleigh Burke-class destroyer,Nimitz-class aircraft carrier,container,and car carrier,are necessary.However,the overall color,shape,and texture of different types of ship targets are similar.The structures of ships belong to different types,but their uses are similar.Moreover,the coating color of military ships is monotonous.These characteristics complicate the classification of these targets.The existing ship detectors are designed to focus on locating targets.The design of the classification branch of these detectors is relatively simple.They only use the features of whole targets for classification,significantly decreasing the performance in the fine-grained labeled datasets.The existing ship classification methods,which mainly classify targets on the pre-cropped image patches,are separated from the detection process.This approach is unsatisfactory for practical applications for two reasons:1)the whole backbone of these methods based on neural networks must be executed on every proposal to extract features.The remote sensing images of the harbor usually include several ships;thus,the computation cost increases sharply.2)The detection and classification networks are optimized separately,and the parameters of both networks are opt

关 键 词：遥感图像 舰船检测 细粒度识别 关键子区域 判别性显著度 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]