• Acta Photonica Sinica
  • Vol. 50, Issue 2, 103 (2021)
Jing CHEN and Zhenxing ZHANG
Author Affiliations
  • College of Information and Electrical Engineering, Ludong University, Yantai, Shandong264000, China
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    DOI: 10.3788/gzxb20215002.0210004 Cite this Article
    Jing CHEN, Zhenxing ZHANG. Greedy Unsupervised Hyperspectral Image Band Selection Method Based on Variable Precision Rough Set[J]. Acta Photonica Sinica, 2021, 50(2): 103 Copy Citation Text show less
    References

    [1] Dongdong XU, Deqiang CHENG, Liangliang CHEN等. Hyperspectral image classification based on hierarchical guided filtering and nearest neighbor regularization subspace. Acta Photonica Sinica, 49, 0410004(2020).

    [2] Jiamin LIU, Limei ZHANG, Guangyao SHI等. Hyperspectral image classification based on sparse property and neighborhood similarity measure. Acta Photonica Sinica, 47, 0610001(2018).

    [3] Jingwen YAN, Hongda CHEN, Lei LIU. Research progress of hyperspectral image classification. Optical Precision Engineering, 27, 680-693(2019).

    [4] Shanxue CHEN, Wenwen CHEN. Combined sparse representation hyperspectral image classification based on secondary dictionary. Systems Engineering and Electronic Technology, 42, 550-556(2020).

    [5] Wei GAO, Yu PENG. Hyperspectral image classification based on Mahalanobis distance multi-core learning. Journal of Instrumentation, 39, 250-257(2018).

    [6] B BARMAN, S PATRA. Soft Computing, 23, 13709-13719(2019).

    [7] Q WANG, F ZHANG, X LI. Optimal clustering framework for hyperspectral band selection. IEEE Transactions on Geoscience & Remote Sensing, 56, 5910-5922(2018).

    [8] Y FAN, C ZHANG, Z LIU. Cost-sensitive stacked sparse autoencoder models to detect striped stem borer infestation on rice based on hyperspectral imaging. Knowledge-based Systems, 168, 49-58(2019).

    [9] B BARMAN, S PATRA. Empirical study of neighbourhood rough sets based band selection techniques for classification of hyperspectral images. IET Image Process, 13, 1266-1279(2019).

    [10] Fuding XIE, Cun LEI, Fangfei LI等. Unsupervised band selection based on fuzzy c-means algorithm and artificial bee colony algorithm. System Science and Mathematics, 38, 1417-1428(2018).

    [11] W XIE, Y LI, W ZHOU. Feature extraction of hyperspectral images with a matting model. International Journal of Remote Sensing, 39, 1510-1527(2018).

    [12] Yue ZHANG, Yunlan GUAN. Unsupervised band selection method of hyperspectral image based on clustering. Beijing Surveying and Mapping, 32, 1-4(2018).

    [13] Liguo WANG, Liang ZHAO, Yao SHI. Band selection of hyperspectral remote sensing image based on the maximum and minimum distance. Journal of Intelligent Systems, 13, 131-137(2018).

    [14] Yao LIU, Zinan LI, Tao WU等. Soybean variety recognition algorithm and comprehensive performance evaluation based on hyperspectral image and neighborhood rough set theory. Soybean Science, 37, 596-605(2018).

    [15] Yao LIU. Research on hyperspectral band selection algorithm based on neighborhood rough set(2017).

    [16] Dan WU, Yan ZHANG. Clustering method of hyperspectral forest biomass based on artificial bee colony optimization algorithm. Laser Journal, 41, 101-104(2020).

    [17] Yuping YIN, Lin WEI, Wanjun LIU. Hyperspectral image classification by integrating cumulative variation ratio and over limit learning machine. Chinese Journal of Image Graphics, 25, 1053-1068(2020).

    [18] Jiayin LI, Wenquan ZHU, Fanyun MENG. Hyperspectral image compression algorithm based on reducing mapping prediction residual error. Computer Engineering and Science, 42, 825-834(2020).

    [19] Binge CUI, Liwei ZHONG, Yan LU. Hyperspectral image classification method based on multi feature image integration. Journal of Shandong University of Science and Technology (Natural Science Edition), 39, 86-94(2020).

    [20] C I CHANG, S WANG. Constrained band selection for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 1575-1585(2006).

    [21] A MARTINEZ-USO, J M SOTOCA et al. Clustering based hyperspectral band selection using information measures. IEEE Transactions on Geoscience and Remote Sensing, 45, 4158-4171(2007).

    Jing CHEN, Zhenxing ZHANG. Greedy Unsupervised Hyperspectral Image Band Selection Method Based on Variable Precision Rough Set[J]. Acta Photonica Sinica, 2021, 50(2): 103
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