Acta Optica Sinica, Volume. 37, Issue 7, 728002(2017)

Spatial-Spectral Semi-Supervised Local Discriminant Analysis for Hyperspectral Image Classification

Hou Banghuan*, Yao Minli, Wang Rong, Zhang Fenggan, and Dai Dingcheng
Author Affiliations
  • [in Chinese]
  • show less
    References(20)

    [1] [1] Fauvel M, Tarabalka Y, Benediktsson A, et al. Advances in spectral-spatial classification of hyperspectral images[C]. Proceedings of the IEEE, 2013, 101(3): 652-675.

    [2] [2] Liu Dawei, Han Ling, Han Xiaoyong. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

    [4] [4] Li Zhimin, Zhang Jie, Huang Hong, et al. Semi-supervised Laplace discriminant embedding for hyperspectral image classification[J]. Journal of Electronics & Information Technology, 2015, 37(4): 995-1001.

    [5] [5] Bandos T, Bruzzone L, Camps-valls G. Classification of hyperspectral images with regularized linear discriminant analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3): 862-873.

    [6] [6] Roweis S, Saul L. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.

    [7] [7] Belkin M, Niyogi P.Laplacian eigenmaps and spectral techniques for embedding and clustering[C]. Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, 2001, 14(6): 585-591.

    [8] [8] He X F, Niyogi P. Locality preserving projections[C]. Proceedings of Advance in Neural Information Processing Systems, 2005, 16(1): 153-160.

    [9] [9] He X F, Cai D, Yan S C, et al. Neighborhood preserving embedding[C]. Proceedings of the 10th IEEE International Conference on Computer Vision, 2005, 2(23): 1208-1213.

    [10] [10] Cai D, He X F, Han J. Semi-supervised discriminant analysis[C]. Proceedings of the 11th IEEE International Conference on Computer Vision, 2008, 5: 1-7.

    [11] [11] Sugiyama M, Ide T, Nakajima S, et al. Semi-supervised local Fisher discriminant analysis for dimensionality reduction[J]. Machine Learning, 2010, 78(1): 36-61.

    [12] [12] Liao W, Pizurica A, Scheunder P, et al. Semisupervised local discriminant analysis for feature extraction in hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 184-198.

    [13] [13] Zhou Y C, Peng J T, Chen C L P.Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 1082-1095.

    [14] [14] Li J, Marpu P R, Plaza A, et al. Generalized composite kernel framework for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(9): 4816-4829.

    [15] [15] Kang X, Li S, Benediktsson J A. Spectral-spatial hyperspectral image classification with edge-preserving filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 2666-2677.

    [16] [16] Li M, Ma A D, Cai J, et al. Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification[J]. Pattern Recogintion Letters, 2016, 83: 133-142.

    [17] [17] Pu H Y, Chen Z, Wang B, et al. A novel spatial-spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11): 7008-7022.

    [19] [19] Hsiuhan L Y, Melba M C. Spectral and spatial proximity-based manifold alignment for multitemporal hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 51-64.

    [20] [20] Dalton L, Saurabh P, Melba M C, et al. Manifold-learning based feature extraction for classification of hyperspectral data: a review of advances in manifold learning[J]. IEEE Signal Processing Magazine, 2014, 31(1): 55-66.

    Tools

    Get Citation

    Copy Citation Text

    Hou Banghuan, Yao Minli, Wang Rong, Zhang Fenggan, Dai Dingcheng. Spatial-Spectral Semi-Supervised Local Discriminant Analysis for Hyperspectral Image Classification[J]. Acta Optica Sinica, 2017, 37(7): 728002

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Remote Sensing and Sensors

    Received: Mar. 9, 2017

    Accepted: --

    Published Online: Jul. 10, 2017

    The Author Email: Banghuan Hou (chinayouth001@aliyun.com)

    DOI:10.3788/aos201737.0728002

    Topics