基于深度学习的高分辨率PolSAR影像暗目标判别

项目名称： 基于深度学习的高分辨率PolSAR影像暗目标判别

项目编号： No.41501382

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 史磊

作者单位： 武汉大学

项目金额： 20万元

中文摘要： 本项目针对SAR影像普遍存在的暗目标混分现象，从目标散射特性与成因出发，开展暗目标的探测、特征学习与识别研究。现有方法在区分暗目标时过于依赖多时相、多源数据，而数据时效性与获取成本制约了该研究的开展；同时，SAR极化信息的利用程度依赖人为经验，人工指定的有效特征数量少，难以适应复杂场景；此外，SAR影像蕴含的场景信息也没有得到充分挖掘，暗目标地物环境语义没有得到充分利用。.针对以上问题，在暗目标判别中将极化与场景特征结合是一个可行的思路。本课题从高分辨率PolSAR目标分解理论出发，构建富含极化、场景信息的高维度散射特征集，采用深度学习剔除特征集冗余信息并发掘其蕴含的极化-场景特征，在反馈学习与后向传播学习的指导下完成暗目标探测与分类，实现不依赖多时相、多源数据的暗目标判别。本项目的开展有望提升SAR影像水泥路、裸土、水体、阴影等典型暗目标解译水平，具有重要的理论意义和应用价值。

中文关键词： PolSAR；暗目标；判别分析；高分辨率；深度学习

英文摘要： This project tries to discriminate the low backscattering object (LBO) by LBO detection, feature representation and LBO distinguish. There are three major drawbacks in current LBO classification methods. Firstly, the LBO is mainly dependent on the temporal and multi-source dataset. But the dataset is usually incompatible and cost which limits the LBO application. Secondly, the LBO distinguish features highly depend on the manual work and the number of features are too few to work well in complex scenes. Furthermore, the scene contextual information of high resolution SAR has not been used..In this project, the polarimetric features and contextual descriptor are combined to discriminate LBO. The high resolution polarimetric SAR is decomposed into dozens of feature by radar decomposition theory. The decomposed features are called feature cube which consists polarimetric and contextual information. Then, the deep learning algorithm is utilized to reduce the redundancy of feature cube and represented by more effective outputs. The feedback and Back-Propagation learning algorithms are also used to improve the LBO distinguish accuracy without the temporal and multi-source data auxiliary. This project will enhance the classification accuracy of LBO, such as cement-road, soil, water and shadow, etc.

英文关键词： PolSAR;Low Backscattering Object ;Discrimination ;High Resolution;Deep Learning