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基于脑启发的PolSAR图像深层协同表示学习与分类

项目来源

国家自然科学基金(NSFC)

项目主持人

侯彪

项目受资助机构

西安电子科技大学

项目编号

61671350

立项年度

2016

立项时间

未公开

研究期限

未知 / 未知

项目级别

国家级

受资助金额

58.00万元

学科

信息科学-电子学与信息系统-信息获取与处理

学科代码

F-F01-F0113

基金类别

面上项目

关键词

深度学习 ; 图像分割 ; 脑启发 ; 表示学习 ; 图像分类 ; image segmentation ; deep learning ; Brain-inspired ; representation learning ; image classification

参与者

唐旭；任博；文载道；吴倩；王建龙；任仲乐；牟树根；张广

参与机构

西北工业大学；中国人民解放军空军工程大学

项目标书摘要：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。

Application Abstract: 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.

项目受资助省

陕西省

项目结题报告(全文)

本项目针对目前有限的物理散射机理驱动的分解模型不能涵盖所有地物类型的散射方式给分类带来的问题，设计自适应PolSAR目标极化分解的新框架，采用堆栈自编码网络自适应的学习极化分解的方式，获得更抽象的极化特征。针对PolSAR地物类型复杂、多样使得目前的特征表示难以自适应性，且缺少完备性与判别性，利用深度卷积置信网络来学习较深层的PolSAR图像特征。针对PolSAR“同类异谱图”和“同谱图异类”问题带来的分类精度下降的问题，考虑将极化特征空间、图像特征空间和语义特征空间进行多模态特征融合，挖掘三个特征空间的关联关系来实现对PolSAR图像的协同表示。将PolSAR数据的分类决策问题建模为一个类标重构的逆问题，通过挖掘类标空间的先验特性，在弱监督学习的算法框架下，建立少量标签指导的特征表示模型，然后设计合适的重构算法，恢复类别标签。建立了基于半监督学习的自适应奇异标签检测方法，对于含噪声类标的数据，采用半监督学习的策略，通过挖掘特征的本征结构先验，然后自适应的根据这种结构得到每个数据对应类标的置信度，以此来区分奇异类标。获教育部自然科学奖二等奖1项，获陕西省科学技术奖一等奖1项，出版专著3部，发表论文47篇，获批国家发明专利19项，依托本项目的研究，我们培养博士生10人，已毕业5人；硕士生30人，已毕业20人。

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  • 1.PolSAR Scene Classification via Low-Rank Tensor-Based Multi-View Subspace Representation

    • 关键词：
    • Matrix algebra;Polarimeters;Synthetic aperture radar;Clustering algorithms;Multimodal features;Polarimetric decomposition;Polarimetric synthetic aperture radars;Representation-matrices;Scene classification;Sub-Space Clustering;Subspace representation;Visual information
    • Chen, Mengqian;Ren, Bo;Hou, Biao;Chanussot, Jocelyn;Wang, Shuang;Zhang, Xiangrong;Xie, Wen
    • 《2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020》
    • 2020年
    • September 26, 2020 - October 2, 2020
    • Virtual, Waikoloa, HI, United states
    • 会议

    In this paper, the polarimetric synthetic aperture radar (PolSAR) scene classification is solved by using a novel low -rank tensor-based multi-view subspace representation (LRT-MSR) method. PolSAR data can be described in multimodal feature spaces, such as PolSAR coherent/covariance/scattering matrices, or the various polarimetric decompositions. Different pseudo-color images from multiple spaces provide enough visual information for making a comprehensive representation. Our method applies a low-rank tensor-based subspace clustering way to explore the information from multi-view pseudo-color images. Tensor, as the high order matrix, is used to capture the correlations of underlying multi-view data. Furthermore, the method is constrained by a low-rank term that elegantly models the cross information from different views, and achieves a series of representation matrices from the redundant information. Finally, a spectral cluster method is used to make the final classification. The experimental results on PolSAR image dataset show the effectiveness of the applied method. © 2020 IEEE.

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  • 2.PolSAR image classification based on DBN and tensor dimensionality reduction

    • 关键词：
    • ;
    • Hou, Biao;Guo, Xianpeng;Hou, Weidan;Wang, Shuang;Zhang, Xiangrong;Jiao, Licheng
    • 《38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018》
    • 2018年
    • July 22, 2018 - July 27, 2018
    • Valencia, Spain
    • 会议

    This paper proposes a new semi-supervised PolSAR image classification method using deep belief network (DBN) and tensor dimensionality reduction, which uses multilinear principle component analysis (MPCA) to reduce the dimension of tensor form PolSAR data, and regards the multiple features of PolSAR data as the input of DBN. In order to take full advantage of neighborhood information of each pixel of PolSAR data, we take each pixel and its neighborhood as tensor form. For PolSAR data, simple feature has been proven not to be able to effectively classify complex terrains. Therefore, we combine multiple features of PolSAR data to obtain more abundant information, which can reflect some spatial structure of PolSAR data. The experimental results show that the overall classification accuracy based on the proposed method outperforms the traditional classification strategies.
    © 2018 IEEE

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