Acta Optica Sinica, Volume. 38, Issue 8, 0828001(2018)

Hyperspectral Remote Sensing Image Classification Based on Three-Dimensional Convolution Neural Network Combined with Conditional Random Field Optimization

Zhuqiang Li1、*, Ruifei Zhu1,2, Fang Gao1, Xiangyu Meng3, Yuan An1, and Xing Zhong1
Author Affiliations
  • 1 Chang Guang Satellite Technology Co.Ltd., Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Changchun, Jilin 130000, China
  • 2 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China
  • 3 Jilin Provincial Land Survey & Planning Institute, Changchun, Jilin 130061, China;
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    References(24)

    [2] Li D R, Zhang L P, Xia G S. Automatic analysis and mining of remote sensing big data[J]. Acta Geodaetica et Cartographica Sinica, 43, 1211-1216(2014).

    [3] Sun L, Wu Z B, Liu J J et al. Supervised spectral-spatial hyperspectral image classification with weighted Markov random fields[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 1490-1503(2015).

    [4] Du P J, Xia J S, Xue Z H et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 20, 236-256(2016).

    [7] Samaniego L, Bardossy A, Schulz K. Supervised classification of remotely sensed imagery using a modified κ-NN technique[J]. IEEE Transactions on Geoscience and Remote Sensing, 46, 2112-2125(2008).

    [9] Li L, Dong Z L. Color image segmentation using improved graph cuts[J]. Geomatics and Information Science of Wuhan University, 39, 1504-1508(2014).

    [10] Zhu S P, Yang L. Stereomatching algorithm with graph cuts based on adaptive watershed[J]. Acta Optica Sinica, 33, 0315004(2013).

    [11] Wu J F, Jiang Z G, Zhang H P et al. Hyperspectral remote sensing image classification based on semi-supervised conditional random field[J]. Journal of Remote Sensing, 21, 588-603(2017).

    [12] Jia K, Li Q Z, Tian Y C et al. A review of classification methods of remote sensing imagery[J]. Spectroscopy and Spectral Analysis, 31, 2618-2623(2011).

    [13] Liu J W, Liu Y, Luo X L. Semi-supervised learning methods[J]. Chinese Journal of Computers, 38, 1592-1617(2015).

    [14] Song L, Cheng Y M, Zhao Y Q. Hyper-spectrum classification based on sparse representation model and auto-regressive model[J]. Acta Optica Sinica, 32, 0330003(2012).

    [15] Dong A G, Li J X, Zhang B et al. Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J]. Acta Optica Sinica, 37, 0828005(2017).

    [17] Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 36, 0428001(2016).

    [19] Mei S H, Ji J Y, Hou J H et al. Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 4520-4533(2017).

    [21] Hu W, Huang Y Y, Wei L et al[J]. Deep convolutional neural networks for hyperspectral image classification Journal of Sensors, 2015, 1-12.

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    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

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Jan. 29, 2018

    Accepted: Apr. 2, 2018

    Published Online: Sep. 6, 2018

    The Author Email:

    DOI:10.3788/AOS201838.0828001

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