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CN 34-1054/N

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Multifeature hyperspectral image classification based on adaptive kernel joint sparse representation

  • School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China

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https://doi.org/10.3969/j.issn.0253-2778.2018.04.005
  • Received Date: 27 May 2017
  • Rev Recd Date: 24 June 2017
  • Publish Date: 30 April 2018
    Abstract

  • Abstract

    Sparse representation has proved to be a powerful tool in hyperspectral image (HSI) classification, and the advantages of joint classification using multifeature information have also attracted in HSI classification field. However, the sparse strategy of multifeature data and the non-linearity in data are two difficult problems. A kernel adaptive sparse model is proposed to classify hyperspectral images. For several complementary features (gradient, texture and shape), the proposed model simultaneously obtains the representation vector for each feature, and utilizes the adaptive sparse strategy ladaptive,0 to effectively use the multifeature information. The adaptive sparse strategy not only limits the representation of pixels in different feature spaces by atoms from a particular class, but also allows the selected atoms of these pixels to be different, thus providing a better representation. In addition, the proposed kernel joint sparse representation model is used to deal with non-linear problems of data. The kernel model projects data into high-dimensional space to improve separability and achieve better performance than linear models. The experimental results of the Indian Pines and University of Pavia show that the proposed algorithm exhibits a higher classification accuracy.

    Abstract

    Sparse representation has proved to be a powerful tool in hyperspectral image (HSI) classification, and the advantages of joint classification using multifeature information have also attracted in HSI classification field. However, the sparse strategy of multifeature data and the non-linearity in data are two difficult problems. A kernel adaptive sparse model is proposed to classify hyperspectral images. For several complementary features (gradient, texture and shape), the proposed model simultaneously obtains the representation vector for each feature, and utilizes the adaptive sparse strategy ladaptive,0 to effectively use the multifeature information. The adaptive sparse strategy not only limits the representation of pixels in different feature spaces by atoms from a particular class, but also allows the selected atoms of these pixels to be different, thus providing a better representation. In addition, the proposed kernel joint sparse representation model is used to deal with non-linear problems of data. The kernel model projects data into high-dimensional space to improve separability and achieve better performance than linear models. The experimental results of the Indian Pines and University of Pavia show that the proposed algorithm exhibits a higher classification accuracy.
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    • References(16)
  • [1]
    CAMPBELL J B. Introduction to Remote Sensing[M]. London: Taylor and Francis, 2000.
    [2]
    MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience & Remote Sensing, 2004, 42(8): 1778-1790.
    [3]
    YANG H. A back-propagation neural network for mineralogical mapping from AVIRIS data[J]. International Journal of Remote Sensing, 1999, 20(1): 97-110.
    [4]
    VAIPHASA C. Innovative genetic algorithm for hyperspectral image classification[EB/OL]. [2017-5-24] Proceedings of the International Conference on MAP ASIA. 2003.
    [5]
    TARABALKA Y, BENEDIKTSSON J A, CHANUSSOT J. Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques[J]. IEEE Transactions on Geoscience & Remote Sensing 2009, 47(8): 2973-2987.
    [6]
    LI J, MARPU P R,BIOUCAA-DIAS J M, et al. Generalized composite kernel framework for hyperspectral image classification[J] IEEE Transactions on Geoscience & Remote Sensing, 2013, 51(9): 4816-4829.
    [7]
    FAUVEL M, TARABALKA Y，BENEDIKTSSON J A, et al. Advances in spectral-spatial classification of hyperspectral images[J]. Proceedings of the IEEE，2013，101(3): 652-675.
    [8]
    LI, J Y, ZHANG H Y, ZHANG L P, et al. Joint collaborative representation with multitask learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(9): 5923-5936.
    [9]
    PENG Y, MENG D Y, XU Z B, et al. Decomposable nonlocal tensor dictionary learning for multispectral image denoising[C]// IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE Press, 2014: 2949-2956.
    [10]
    CHEN Y, NASRABADI N M, TRAN T D. Hyperspectral image classification using dictionary-based sparse representation[J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(10): 3973-3985.
    [11]
    YUAN X T, LIU X, YAN S. Visual classification with multitask joint sparse representation[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2012, 21(10): 4349-4360.
    [12]
    CAMPS-VALLS G, GOMEZ-CHOVA L, MUNOZ-MARI J, et al. Composite kernels for hyperspectral image classification[J]. Geoscience & Remote Sensing Letters, 2006, 3(1): 93-97.
    [13]
    赵振凯. 结合紧邻选择的高光谱图像分类算法研究[D]. 南京: 南京师范大学, 2016.
    [14]
    ZHANG, E L, ZHANG X R, JIAO L C et al. Weighted multifeature hyperspectral image classification via kernel joint sparse representation[J]. Neurocomputing, 2016, 178(C): 71-86.
    [15]
    MO X, MONGA V, BALA R, et al. Adaptive sparse representations for video anomaly detection[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2014, 24(4): 631-645.
    [16]
    YANG M, ZHANG L, ZHANG D, et al. Relaxed collaborative representation for pattern classification[C]// IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE Computer Society, 2012: 2224-2231.
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    [1]
    CAMPBELL J B. Introduction to Remote Sensing[M]. London: Taylor and Francis, 2000.
    [2]
    MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience & Remote Sensing, 2004, 42(8): 1778-1790.
    [3]
    YANG H. A back-propagation neural network for mineralogical mapping from AVIRIS data[J]. International Journal of Remote Sensing, 1999, 20(1): 97-110.
    [4]
    VAIPHASA C. Innovative genetic algorithm for hyperspectral image classification[EB/OL]. [2017-5-24] Proceedings of the International Conference on MAP ASIA. 2003.
    [5]
    TARABALKA Y, BENEDIKTSSON J A, CHANUSSOT J. Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques[J]. IEEE Transactions on Geoscience & Remote Sensing 2009, 47(8): 2973-2987.
    [6]
    LI J, MARPU P R,BIOUCAA-DIAS J M, et al. Generalized composite kernel framework for hyperspectral image classification[J] IEEE Transactions on Geoscience & Remote Sensing, 2013, 51(9): 4816-4829.
    [7]
    FAUVEL M, TARABALKA Y，BENEDIKTSSON J A, et al. Advances in spectral-spatial classification of hyperspectral images[J]. Proceedings of the IEEE，2013，101(3): 652-675.
    [8]
    LI, J Y, ZHANG H Y, ZHANG L P, et al. Joint collaborative representation with multitask learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(9): 5923-5936.
    [9]
    PENG Y, MENG D Y, XU Z B, et al. Decomposable nonlocal tensor dictionary learning for multispectral image denoising[C]// IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE Press, 2014: 2949-2956.
    [10]
    CHEN Y, NASRABADI N M, TRAN T D. Hyperspectral image classification using dictionary-based sparse representation[J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(10): 3973-3985.
    [11]
    YUAN X T, LIU X, YAN S. Visual classification with multitask joint sparse representation[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2012, 21(10): 4349-4360.
    [12]
    CAMPS-VALLS G, GOMEZ-CHOVA L, MUNOZ-MARI J, et al. Composite kernels for hyperspectral image classification[J]. Geoscience & Remote Sensing Letters, 2006, 3(1): 93-97.
    [13]
    赵振凯. 结合紧邻选择的高光谱图像分类算法研究[D]. 南京: 南京师范大学, 2016.
    [14]
    ZHANG, E L, ZHANG X R, JIAO L C et al. Weighted multifeature hyperspectral image classification via kernel joint sparse representation[J]. Neurocomputing, 2016, 178(C): 71-86.
    [15]
    MO X, MONGA V, BALA R, et al. Adaptive sparse representations for video anomaly detection[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2014, 24(4): 631-645.
    [16]
    YANG M, ZHANG L, ZHANG D, et al. Relaxed collaborative representation for pattern classification[C]// IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE Computer Society, 2012: 2224-2231.
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