多模态特征学习与协同表示和鲁棒的分类器设计是极化SAR分类中重要的两个核心问题。然而大规模极化SAR的数据量中通常仅有少量有效的类别标签，给这两个问题的有效解决带来了困难。本项目受到大脑感知、学习表示与决策融合过程的启发，构造一个多模块的深层神经网络来逐层解决以上问题。首先，本项目构造两个并行的低级感知皮层网络实现无监督的学习PolSAR图像特征与极化特征；其次，在上述两个低级网络之上，模拟构造高级前额皮层网络将底层多源特征进行协同表示与融合；最后，在高层网络中加入语义先验知识以及少量的监督信息，在贝叶斯推理的框架下实现决策分类，并用RADARSAT2和PALSAR的全极化SAR数据验证其有效性。期望在提高极化SAR图像分类效果的同时，进一步完善和促进深度学习等理论的研究和应用。成果在本领域重要期刊和会议上发表论文15-20篇，申报国家发明专利6-8项，联合培养博士、硕士5-8名。

Multimodal feature learning and collaborative representation, together with a robust classifier construction are two crucial problems in PolSAR classification. However, large scale of PolSAR data with only a few labelled information increases their difficulties. Inspired by human brain of sensing, learning representation and decision fusion process. This project aims at constructing a deep neural network consisting of several basic networks to address these issues in a layer-wise manner. Firstly, we construct two networks imitating the low level sensing the cerebral cortex in order to respectively learn the PolSAR image and polarimetric features in parallel without supervised information. These features will be subsequently fused in a higher level network which performs the same role as the prefrontal cortex. By incorporating several semantic prior knowledge as well as a limited supervised information in the high-level network, the classification can be implemented via Bayesian inference. We validate the effectiveness of the proposed method by the full-polarimetric data of RADARSAT 2 and PALSAR. We hope our methods not only to improve performance of Polarimetric SAR image classification, but also further boosting and carving out compressed sensing theory and its applications. We will publish 10-15 journals and conferences, apply 6-8 patents and bring up 5-8 Ph.Ds and masters.