2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于近邻协同的高光谱图像谱-空联合分类

倪鼎 马洪兵

倪鼎, 马洪兵. 基于近邻协同的高光谱图像谱-空联合分类. 自动化学报, 2015, 41(2): 273-284. doi: 10.16383/j.aas.2015.c140043
引用本文: 倪鼎, 马洪兵. 基于近邻协同的高光谱图像谱-空联合分类. 自动化学报, 2015, 41(2): 273-284. doi: 10.16383/j.aas.2015.c140043
NI Ding, MA Hong-Bing. Spectral-spatial Classification of Hyperspectral Images Based on Neighborhood Collaboration. ACTA AUTOMATICA SINICA, 2015, 41(2): 273-284. doi: 10.16383/j.aas.2015.c140043
Citation: NI Ding, MA Hong-Bing. Spectral-spatial Classification of Hyperspectral Images Based on Neighborhood Collaboration. ACTA AUTOMATICA SINICA, 2015, 41(2): 273-284. doi: 10.16383/j.aas.2015.c140043

基于近邻协同的高光谱图像谱-空联合分类

doi: 10.16383/j.aas.2015.c140043
基金项目: 

国家高技术研究发展计划(863计划)(2007AA12Z149)资助

详细信息
    作者简介:

    马洪兵 清华大学电子工程系副教授.1999 年获北京大学博士学位. 主要研究方向为图像处理, 模式识别, 空间信息处理与应用.E-mail: hbma@mail.tsinghua.edu.cn

    通讯作者:

    倪鼎 清华大学电子工程系博士研究生. 2012 年获得华中科技大学电子与信息工程系学士学位. 主要研究方向为遥感图像处理, 高光谱分类, 信息处理, 模式识别. 本文通信作者.E-mail: nid12@mails.tsinghua.edu.cn

Spectral-spatial Classification of Hyperspectral Images Based on Neighborhood Collaboration

Funds: 

Supported by National High Technology Research and Development Program of China (863 Program) (2007AA12Z149)

  • 摘要: 遥感高光谱成像能够获得丰富的地物光谱信息, 为高精度的地物分析提供了可能. 针对高光谱图像分类中通常面临的数据维数高、标记样本少、计算量大等问题, 提出了一种简单有效的谱--空联合分类方法. 利用高光谱图像丰富的光谱信息和地物分布的空间平滑特性, 该算法首先对光谱数据进行特征提取和空间滤波, 然后利用本文提出的基于近邻协同的支持向量机(Neighborhood collaborative support vector machine, NC-SVM)进行分类. 近邻协同进一步利用地物分布的空间平滑特性, 通过联合空间近邻的判决信息进行中心像素的类别判定, 有效减小了只有少量训练样本下的错分概率. 实验表明, 相比已有的相关方法, 该算法在不明显增加计算量的情况下可获得更高的分类正确率, 能够实现少量训练样本下高光谱图像的快速高精度分类.
  • [1] Shaw G, Manolakis D. Signal processing for hyperspectral image exploitation. Signal Processing Magazine, 2002, 19(1): 12-16
    [2] He Lin, Pan Quan, Di Wei, Li Yuan-Qing. Supervised detection for hyperspectral imagery based on high-dimensional multiscale autoregression. Acta Automatica Sinica, 2009, 35(5): 509-518(贺霖, 潘泉, 邸韡, 李远清. 高光谱图像高维多尺度自回归有监督检测. 自动化学报, 2009, 35(5): 509-518)
    [3] Wang Yan-Qing, Ma Lei, Tian Yuan. State-of-the-art of ship detection and recognition in optical remotely sensed imagery. Acta Automatica Sinica, 2011, 37(9): 1029-1039(王彦情, 马雷, 田原. 光学遥感图像舰船目标检测与识别综述. 自动化学报, 2011, 37(9): 1029-1039)
    [4] Su Juan, Wang Gui-Jin, Lin Xing-Gang, Liu Dai-Zhi. A change detection algorithm for man-made objects based on multi-temporal remote sensing images. Acta Automatica Sinica, 2008, 34(9): 1040-1046(苏娟, 王贵锦, 林行刚, 刘代志. 基于多时相遥感图像的人造目标变化检测算法. 自动化学报, 2008, 34(9): 1040-1046)
    [5] [5] Hughes G. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 1968, 14(1): 55-63
    [6] Zhang Xue-Gong. Introduction to statistical learning theory and support vector machines. Acta Automatica Sinica, 2000, 26(1): 32-42(张学工. 关于统计学习理论与支持向量机. 自动化学报, 2000, 26(1): 32-42)
    [7] [7] Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Vila-Frances J, Calpe-Maravilla J. Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1): 93-97
    [8] [8] Gurram P, Heesung K. Contextual SVM using Hilbert space embedding for hyperspectral classification. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1031-1035
    [9] [9] Fauvel M, Benediktsson J A, Chanussot J, Sveinsson J R. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11): 3804-3814
    [10] Tarabalka Y, Fauvel M, Chanussot J, Benediktsson J A. SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 2010, 7(4): 736-740
    [11] Tan Kun, Du Pei-Jun, Wang Xiao-Mei. Impacts of feature dimensionality to the support vector machine classifier for hyperspectral remote sensing image. Science of Surveying and Mapping, 2011, 36(1): 55-57(谭琨, 杜培军, 王小美. 特征维数对支持向量机分类器性能影响的研究. 测绘科学, 2011, 36(1): 55-57)
    [12] Chen Hong-Da, Pu Han-Ye, Wang Bin, Zhang Li-Ming. Image Euclidean distance-based manifold dimensionality reduction algorithm for hyperspectral imagery. Journal of Infrared and Millimeter Waves, 2013, 32(5): 450-455(陈宏达, 普晗哗, 王斌, 张立明. 基于图像欧氏距离的高光谱图像流形降维算法. 红外与毫米波学报, 2013, 32(5): 450-455)
    [13] Kuo B C, Landgrebe D A. Nonparametric weighted feature extraction for classification. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(5): 1096-1105
    [14] Chang Y L, Liu J N, Han C C, Chen Y N. Hyperspectral image classification using nearest feature line embedding approach. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 278-287
    [15] Li W, Prasad S, Fowler J E, Bruce L M. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1185-1198
    [16] Shi Q, Hang L P, Du B. Semisupervised discriminative locally enhanced alignment for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(9): 4800-4815
    [17] Ly N H, Du Q, Fowler J E. Sparse graph-based discriminant analysis for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(7): 3872-3884
    [18] Fauvel M, Tarabalka Y, Benediktsson J A, Chanussot J, Tilton J C. Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE, 2013, 101(3): 652-675
    [19] Gao Xiao-Jian, Guo Bao-Feng, Yu Ping. Classification of hyperspectral remote sensing image based on spatial-spectral integration. Laser and Infrared, 2013, 43(11): 1296-1300(高晓建, 郭宝峰, 于平. 高光谱空谱一体化图像分类研究. 激光与红外, 2013, 43(11): 1296-1300)
    [20] Gao Heng-Zhen, Wan Jian-Wei, Wang Li-Bao, Xu Zhan. Research on classification technique for hyperspectral imagery based on spectral-spatial composite kernels. Signal Processing, 2011, 27(5): 648-652(高恒振, 万建伟, 王力宝, 徐湛. 基于谱域--空域组合核函数的高光谱图像分类技术研究. 信号处理, 2011, 27(5): 648-652)
    [21] Qian Y T, Ye M C, Zhou J. Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4): 2276-2291
    [22] Tarabalka Y, Benediktsson J A, Chanussot J. Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(8): 2973-2987
    [23] Bai J, Xiang S M, Pan C H. A graph-based classification method for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 803-817
    [24] Gustavo C V, Shervashidze N, Borgwardt K M. Spatio-spectral remote sensing image classification with graph kernels. IEEE Geoscience and Remote Sensing Letters, 2010, 7(4): 741-745
    [25] Li W, Tramel E W, Prasad S, Fowler J E. Nearest regularized subspace for hyperspectral classification. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 477 -489
    [26] Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the 6th International Conference on Computer Vision. Bombay, India: IEEE, 1998. 839-846
    [27] Cristianini N, John S T. An Introduction to Support Vector Machines: and Other Kernel-Based Learning Methods. Cambridge: Cambridge University Press, 2000.
    [28] Tadjudin S, Landgrebe D A. Covariance estimation with limited training samples. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(4): 2113-2118
    [29] Richards J A, Jia X. Remote Sensing Digital Image Analysis: An Introduction. New York: Springer-Verlag, 2006.
    [30] Ye J. Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. Journal of Machine Learning Research, 2005, 6(4): 483-502
    [31] Bandos T V, Bruzzone L, Camps-Valls G. Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3): 862-873
    [32] Sugiyama M. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. Journal of Machine Learning Research, 2007, 8(5): 1027-1061
  • 加载中
计量
  • 文章访问数:  2174
  • HTML全文浏览量:  121
  • PDF下载量:  1190
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-01-16
  • 修回日期:  2014-05-15
  • 刊出日期:  2015-02-20

目录

    /

    返回文章
    返回