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基于推广流形学习的高分辨遥感影像目标分类

郭亚宁 林伟 潘泉 赵春晖 胡劲文 马娟娟

郭亚宁, 林伟, 潘泉, 赵春晖, 胡劲文, 马娟娟. 基于推广流形学习的高分辨遥感影像目标分类. 自动化学报, 2019, 45(4): 720-729. doi: 10.16383/j.aas.2017.c170318
引用本文: 郭亚宁, 林伟, 潘泉, 赵春晖, 胡劲文, 马娟娟. 基于推广流形学习的高分辨遥感影像目标分类. 自动化学报, 2019, 45(4): 720-729. doi: 10.16383/j.aas.2017.c170318
GUO Ya-Ning, LIN Wei, PAN Quan, ZHAO Chun-Hui, HU Jin-Wen, MA Juan-Juan. Generalized Manifold Learning for High Resolution Remote Sensing Image Object Classification. ACTA AUTOMATICA SINICA, 2019, 45(4): 720-729. doi: 10.16383/j.aas.2017.c170318
Citation: GUO Ya-Ning, LIN Wei, PAN Quan, ZHAO Chun-Hui, HU Jin-Wen, MA Juan-Juan. Generalized Manifold Learning for High Resolution Remote Sensing Image Object Classification. ACTA AUTOMATICA SINICA, 2019, 45(4): 720-729. doi: 10.16383/j.aas.2017.c170318

基于推广流形学习的高分辨遥感影像目标分类

doi: 10.16383/j.aas.2017.c170318
基金项目: 

陕西省自然科学基金 2017JQ6005

航空科学基金 2014ZC53030

中央大学基础研究基金 3102017jg02011

中国博士后科学基金 2017M610650

陕西省自然科学基金 2017JM6027

国家自然科学基金 61603303

国家自然科学基金 61473230

详细信息
    作者简介:

    郭亚宁  西北工业大学自动化学院博士研究生.主要研究方向为遥感图像处理, 无人机自主防撞感知规避.E-mail:yaningg0924@mail.nwpu.edu.cn

    潘泉  西北工业大自动化学院教授.1997年获得西北工业大学博士学位.主要研究方向为信息融合理论及应用, 目标跟踪与识别技术, 光谱成像及图像处理.E-mail:quanpan@nwpu.edu.cn

    赵春晖  西北工业大自动化学院副教授.2008年获得西北工业大学博士学位.主要研究方向为视频图像处理, 目标跟踪识别, 无人机视觉导航.E-mail:zhaochunhui@nwpu.edu.cn

    胡劲文  西北工业大自动化学院副教授.2013年获得新加坡南洋理工大学博士学位.主要研究方向为网络控制与滤波.E-mail:hujinwen@nwpu.edu.cn

    马娟娟  西北工业大学自动化学院博士研究生.主要研究方向为目标跟踪与识别技术.E-mail:majuanjuan903@163.com

    通讯作者:

    林伟  西北工业大学理学院副教授.2007年获得西北工业大学博士学位.主要研究方向为统计建模与遥感图像处理.本文通信作者.E-mail:linwei@nwpu.edu.cn

Generalized Manifold Learning for High Resolution Remote Sensing Image Object Classification

Funds: 

Natural Science Foundation of Shaanxi Province 2017JQ6005

Aviation Science Foundation 2014ZC53030

Fundamental Research Funds for the Central Universities 3102017jg02011

China Postdoctoral Science Foundation 2017M610650

Natural Science Foundation of Shaanxi Province 2017JM6027

National Natural Science Foundation of China 61603303

National Natural Science Foundation of China 61473230

More Information
    Author Bio:

     Ph. D. candidate at the School of Automation, Northwestern Polytechnical University. Her research interest covers remote sensing image processing and autonomous collision avoidance, sense and avoid technologies for unmanned aerial vehicles (UAVs)

     Professor at the School of Automation, Northwestern Polytechnical University. He received his Ph. D. degree from Northwestern Polytechnical University in 1997. His research interest covers information fusion theory and application, target tracking and recognition technology, and spectral imaging and image processing

     Associate professor at the School of Automation, Northwestern Polytechnical University. He received his Ph. D. degree from Northwestern Polytechnical University in 2008. His research interest covers video image processing, target tracking recognition, and unmanned aerial vehicle visual navigation

     Associate professor at the School of Automation, Northwestern Polytechnical University. He received his Ph. D. degree from Singapore Nanyang Technological University (NTU) in 2013. His research interest covers network control and flltering

     Ph. D. candidate at the School of Automation, Northwestern Polytechnical University. Her research interest covers target tracking and recognition technology

    Corresponding author: LIN Wei  Associate professor at the School of Natural and Applied Sciences, Northwestern Polytechnical University. She received her Ph. D. degree from Northwestern Polytechnical University in 2007. Her research interest covers statistical modeling and remote sensing image processing. Corresponding author of this paper
  • 摘要: 针对传统的流形学习算法不能对位于黎曼流形上的协方差描述子进行有效降维这一问题,本文提出一种推广的流形学习算法,即基于Log-Euclidean黎曼核的自适应半监督正交局部保持投影(Log-Euclidean Riemannian kernel-based adaptive semi-supervised orthogonal locality preserving projection,LRK-ASOLPP),并将其成功用于高分辨率遥感影像目标分类问题.首先,提取图像每个像素点处的几何结构特征,计算图像特征的协方差描述子;其次,通过采用Log-Euclidean黎曼核将协方差描述子投影到再生核Hilbert空间;然后,基于流形学习理论,建立黎曼流形上半监督正交局部保持投影算法模型,利用交替迭代更新算法对目标函数进行优化求解,同时获得相似性权矩阵和低维投影矩阵;最后,利用求得的低维投影矩阵计算测试样本的低维投影,并用K—近邻、支持向量机(Support victor machine,SVM)等分类器对其进行分类.三个高分辨率遥感影像数据集上的实验结果说明了该算法的有效性与可行性.
    1)  本文责任编委 胡清华
  • 图  1  不同近邻数k对应的分类精度

    Fig.  1  Classiflcation accuracy for difierent values of k

    图  2  三维特征可视化图

    Fig.  2  3D feature visualization

    图  3  最佳分类精度随特征维数变化曲线图

    Fig.  3  The varying curves of the optimal classiflcation accuracy with feature dimension

    图  4  三个数据集上不同训练样本数算法最佳平均分类精度

    Fig.  4  The average classification accuracy of different training sample number on three datasets

    表  1  最佳分类精度(Ac)及对应特征维数($r$)

    Table  1  The classification accuracy (Ac) and the corresponding feature dimension ($r$)

    数据集UCMercedWHU-RSQuick bird
    算法Ac (%) $r$Ac (%) $r$Ac (%) $r$
    LRK-SOLPP92.324592.682591.8745
    KLPP93.152093.645092.8425
    LRK-ASOLPP 94.89 35 96.43 25 95.6920
    下载: 导出CSV

    表  2  UCMerced LandUse dataset上的最佳分类精度(Ac)及对应特征维数(r)

    Table  2  The classiflcation accuracy (Ac) and the feature dimension (r) on UCMerced LandUse dataset

    算法LRK-SOLPPKLPPLRK-ASOLPP
    分类器Ac(%) $r$Ac(%) $r$Ac(%) $r$
    K-NN84.452084.382090.1835
    K-means85.652089.062591.1625
    SVM87.251590.761594.2720
    BP-ANN 89.82 20 91.64 20 95.3425
    下载: 导出CSV

    表  3  WHU-RS dataset上的最佳分类精度(Ac)及对应特征维数(r)

    Table  3  The classiflcation accuracy (Ac) and the feature dimension (r) on WHU-RS dataset

    算法LRK-SOLPPKLPPLRK-ASOLPP
    分类器Ac(%) $r$Ac(%) $r$Ac(%) $r$
    K-NN85.323088.042090.2520
    K-means88.582089.645090.8725
    SVM87.6835 91.76 1595.7920
    BP-ANN 90.47 2090.4320 96.1825
    下载: 导出CSV

    表  4  Quick bird dataset上的最佳分类精度(Ac)及对应特征维数(r)

    Table  4  The classiflcation accuracy (Ac) and the feature dimension (r) on Quick bird dataset

    算法LRK-SOLPPKLPPLRK-ASOLPP
    分类器Ac(%) $r$Ac(%) $r$Ac(%) $r$
    K-NN84.982593.652094.1835
    K-means86.622092.065096.2825
    SVM88.762590.3825 93.6920
    BP-ANN 89.45 20 92.56 3095.8925
    下载: 导出CSV
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  • 收稿日期:  2017-06-14
  • 录用日期:  2017-11-24
  • 刊出日期:  2019-04-20

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