基于Exemplar-Classifier思想的高分辨率光学遥感影像目标识别研究

Target recognition plays an important role in automatic interpretation of high-resolution optical remote sensing imagery (HR RS imagery). False negative and false positive rates are two major issues for target recognition method. Low false negative rate requires good generalization ability of method and low false positive rate can only be obtained by high threshold. Therefore, it is difficult to make both the false negative and the false positive rate low. The state-of-the-art in target recognition research demonstrated that Exemplar and Classifier can be integrated to achieve this goal. We propose to introduce the Exemplar-Classifier method to target recognition of HR RS imagery. To accomplish this, two issues need to be addressed. Firstly, the various imaging conditions can greatly increase the size of the positive training set, which can lead to huge computation cost due to the large number of classifiers created for each positive sample. Non-leaner dimension reduction and cluster techniques will be employed to reduce the size of the positive training set and optimize the structure of it as well. The second issue is the difficulty in characterizing the complex target due to the high resolution of imagery. A multi-level deformable parts model will be developed to solve this problem. By addressing the above two issues, a novel method that has the advantages of both the Exemplar-based and Classifier-based methods for target recognition in HR RS imagery will be developed. The new method will be able to reduce both the false positive rate and the false negative rate. The proposed method will be significant for automatic interpretation of HR RS imagery.

目标识别是高分遥感影像解译的核心环节，漏检率和虚警率是目标识别方法的两项主要评价指标，前者要求好的推广能力，后者要求高的严格性，因此现有方法很难对两者进行兼顾。最新成果表明，基于Exemplar-Classifier思想对目标识别中的模板和分类方法进行集成，可使目标识别方法兼具模板方法的严格性和分类方法的推广性，同时降低漏检率和虚警率。目前该思想只应用于计算机视觉领域，在遥感领域尚无应用。申请者提出将该思想引入高分遥感影像目标识别中，通过两个关键问题的解决实现其应用。首先基于非线性降维、聚类等手段研究样本约简和优化组织方法，解决高分遥感影像成像状态多样性带来的正样本集膨胀和构建困难问题；然后基于尺度和空间信息，建立多层次局部可变模型，解决影像超高分辨率引起的复杂目标描述困难问题。最终，得到一种能够同时改善虚警率和漏检率的高分遥感影像目标识别新方法，这对高分遥感影像自动解译具有重要意义。

项目基于Exemplar-Classifier思想，开展了高分辨率光学遥感影像的目标识别研究，完成了针对聚集船只、桥梁、航标、港口、机场等目标的识别研究。为了支持目标识别研究，还将支持向量机与主动轮廓模型进行融合，完成了高分辨率遥感影像精细分割的研究。在研究中，通过借鉴深度学习的思路，完成了基于可区分性中层描述特征提取在机场、港口、船只检测中的应用。研究了可变形部件模型与中层特征结合的目标检测方法。最后研究了深度卷积神经网络在船只稳定检测方面的应用。. 项目的大部分成果成功应用于国家对地观测科技重大专项课题“高分综合交通遥感应用示范系统（一期）（07-Y30B10-9001-14/16）”。