• Acta Optica Sinica
  • Vol. 37, Issue 8, 0828005 (2017)
Anguo Dong1, Jiaxun Li1、*, Bei Zhang1, and Miaomiao Liang2
Author Affiliations
  • 1 School of Science, Chang'an University, Xi'an, Shaanxi 710064, China
  • 2 School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, China
  • show less
    DOI: 10.3788/AOS201737.0828005 Cite this Article Set citation alerts
    Anguo Dong, Jiaxun Li, Bei Zhang, Miaomiao Liang. Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation[J]. Acta Optica Sinica, 2017, 37(8): 0828005 Copy Citation Text show less
    References

    [1] Liu Dawei, Han Ling, Han Xiaoyong. High spatial resolution remote densing image classification based on deep learning[J]. Acta Optica Sinica, 36, 0428001(2016).

    [2] Liang J Y, Zhao X W, Li D Y et al. Determining the number of clusters using information entropy for mixed data[J]. Pattern Recognition, 45, 2251-2265(2012). http://www.sciencedirect.com/science/article/pii/S0031320311005188

    [3] Ishidoshiro N, Yamaguchi Y, Noda S, Remote Sensing, Spatial Information Sciences et al. XLI-, B8, 431-435(2016).

    [4] Xu Jie, Zhang Jianqi, Liu Delian et al. Classification of hyperspectral image with FSVM[J]. Optical Technique, 34, 138-140(2008).

    [5] Gao L R, Li J, Khodadadzadeh M et al. Subspace-based support vector machines for hyperspectral image classification[J]. IEEE Geoscience & Remote Sensing Letters, 12, 349-353(2015). http://ieeexplore.ieee.org/document/6871364/

    [6] Ratle F, Camps-Valls G, Weston J. Semisupervised neural networks for efficient hyperspectral image classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 48, 2271-2282(2010). http://ieeexplore.ieee.org/document/5411821/

    [7] Fang L Y, Li S T, Kang X D et al. Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation[J]. IEEE Transactions on Geoscience & Remote Sensing, 52, 7738-7749(2014). http://ieeexplore.ieee.org/document/6810793/

    [8] Zhang H Y, Li J Y, Huang Y C et al. A nonlocal weighted joint sparse representation classification method for hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 7, 2056-2065(2014). http://ieeexplore.ieee.org/document/6522858/

    [9] Chen Y, Nasrabadi N M, Tran T D. Hyperspectral image classification using dictionary-based sparse representation[J]. IEEE Transactions on Geoscience & Remote Sensing, 49, 3973-3985(2011). http://ieeexplore.ieee.org/document/5766028/

    [10] Fang L Y, Li S T, Kang X D et al. Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model[J]. IEEE Transactions on Geoscience & Remote Sensing, 53, 4186-4201(2015). http://ieeexplore.ieee.org/document/7042923/

    [11] Camps-Valls G, Bruzzone L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 43, 1351-1362(2005). http://ieeexplore.ieee.org/document/1433032/

    [12] Xie J Y, Hone K, Xie W X et al. Extending twin support vector machine classifier for multi-category classification problems[J]. Intelligent Data Analysis, 17, 649-664(2013). http://dl.acm.org/citation.cfm?id=2595583

    [13] Chen Y, Nasrabadi N M, Tran T D. Hyperspectral image classification via kernel sparse representation[J]. IEEE Transactions on Geoscience & Remote Sensing, 51, 217-231(2013). http://ieeexplore.ieee.org/document/6236130/

    [14] Sun X X, Qu Q, Nasrabadi N M et al. Structured priors for sparse-representation-based hyperspectral image classification[J]. IEEE Geoscience & Remote Sensing Letters, 11, 1235-1239(2014). http://ieeexplore.ieee.org/document/6681879/

    [15] Peng J T, Zhou Y C. Chen C L P. Region-kernel-based support vector machines for hyperspectral image classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 53, 4810-4824(2015). http://ieeexplore.ieee.org/document/7080913

    CLP Journals

    [1] Jian Du, Bingliang Hu, Zhoufeng Zhang. Gastric Carcinoma Classification Based on Convolutional Neural Network and Micro-Hyperspectral Imaging[J]. Acta Optica Sinica, 2018, 38(6): 0617001

    [2] Denggang Li, Zhongmei Wang. Improved Spatial Information Constrained Nonnegative Matrix Factorization Method for Hyperspectral Unmixing[J]. Laser & Optoelectronics Progress, 2019, 56(11): 111006

    [3] Chunyan Yu, Meng Zhao, Meiping Song, Sen Li, Yulei Wang. Hyperspectral Image Classification Method Based on Targets Constraint and Spectral-Spatial Iteration[J]. Acta Optica Sinica, 2018, 38(6): 0628003

    [4] Zhuqiang Li, Ruifei Zhu, Fang Gao, Xiangyu Meng, Yuan An, Xing Zhong. Hyperspectral Remote Sensing Image Classification Based on Three-Dimensional Convolution Neural Network Combined with Conditional Random Field Optimization[J]. Acta Optica Sinica, 2018, 38(8): 0828001

    [5] Denggang Li, Zhongmei Wang. Improved Spatial Information Constrained Nonnegative Matrix Factorization Method for Hyperspectral Unmixing[J]. Laser & Optoelectronics Progress, 2019, 56(11): 111006

    Anguo Dong, Jiaxun Li, Bei Zhang, Miaomiao Liang. Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation[J]. Acta Optica Sinica, 2017, 37(8): 0828005
    Download Citation