[1] 张成业, 秦其明, 陈理, 等. 高光谱遥感岩矿识别的研究进展［J］. 光学 精密工程, 2015, 23(8): 2407-2418.

    ZHANG CH Y, QIN Q M, CHEN L, et al.. Research and development of mineral identification utilizing hyperspectral remote sensing［J］. Opt. Precision Eng., 2015, 23(8): 2407-2418. (in Chinese)

[2] JIAO Q J, ZHANG B, LIU J G, et al.. A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index［J］. International Journal of Remote Sensing, 2014, 35(21): 7363-7375.

[3] 鲍一丹, 陈纳, 何勇, 等. 近红外高光谱成像技术快速鉴别国产咖啡豆品种［J］. 光学 精密工程, 2015, 23(2): 349-355.

    BAO Y D, CHEN N, HE Y, et al.. Rapid identification of coffee bean variety by near infrared hyperspectral imaging technology［J］. Opt. Precision Eng., 2015, 23(2): 349-355. (in Chinese)

[4] 龚小进, 王刚, 欧中华, 等. 高光谱成像技术在生物医学中的应用［J］. 激光生物学报, 2016, 25(4): 289-314.

    GONG X J, WANG G, OU ZH H, et al.. The application of hyperspectral imaging technique in biomedicine［J］. Acta Laser Biology Sinica, 2016, 25(4): 289-314. (in Chinese)

[5] 张兵. 高光谱图像处理与信息提取前沿［J］. 遥感学报, 2016, 20(5): 1062-1090.

    ZHANG B. Advancement of hyperspectral image processing and information extraction［J］. Journal of Remote Sensing, 2016, 20(5): 1062-1090. (in Chinese)

[6] RICHARDS J A, JIA X P. Remote Sensing Digital Image Analysis: an Introduction［M］. 4th ed. Berlin: Springer Verlag, 2006.

[7] DU P J, TAN K, XING X S. A novel binary tree support vector machine for hyperspectral remote sensing image classification［J］. Optics Communications, 2012, 285(13-14): 3054-3060.

[8] 郭文川, 董金磊. 高光谱成像结合人工神经网络无损检测桃的硬度［J］. 光学 精密工程, 2015, 23(6): 1530-1537.

    GUO W CH, DONG J L. Nondestructive detection on firmness of peaches based on hyperspectral imaging and artificial neural networks［J］. Opt. Precision Eng., 2015, 23(6): 1530-1537. (in Chinese)

[9] 周志华. 机器学习［M］. 北京: 清华大学出版社, 2016.

    ZHOU Z H. Machine Learning［M］. Beijing: Tsinghua University Press, 2016. (in Chinese)

[10] QIAN Y T, YE M C, ZHOU J. Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features［J］. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4): 2276-2291.

[11] 唐中奇, 付光远, 陈进, 等. 基于多尺度分割的高光谱图像稀疏表示与分类［J］. 光学 精密工程, 2015, 23(9): 2708-2714.

    TANG ZH Q, FU G Y, CHEN J, et al.. Multiscale segmentation-based sparse coding for hyperspectral image classification［J］. Opt. Precision Eng., 2015, 23(9): 2708-2714. (in Chinese)

[12] 耿修瑞, 张霞, 陈正超, 等. 一种基于空间连续性的高光谱图像分类方法［J］. 红外与毫米波学报, 2004, 23(4): 299-302.

    GENG X R, ZHANG X, CHEN ZH CH, et al.. Classification algorithm based on spatial continuity for hyperspectral image［J］. Journal of Infrared and Millimeter Waves, 2004, 23(4): 299-302. (in Chinese)

[13] TARABALKA Y, CHANUSSOT J, BENEDIKTSSON J A. Segmentaion and classification of hyperspectral images using watershed transformation［J］. Patter Recognition, 2010, 43(7): 2367-2379.

[14] LI S S, JIA X P, ZHANG B. Superpixel-based Markov random field for classification of hyperspectral images［C］. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2013, 3491-3493.

[15] 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 and Remote Sensing, 2015, 53(8): 4186-4201.

[16] 黄鸿, 郑新磊. 加权空-谱与最近邻分类器相结合的高光谱图像分类［J］. 光学 精密工程, 2016, 24(4): 873-881.

    HUANG H, ZHENG X L. Hyperspectral image classification with combination of weighted spatial-spectral and KNN［J］. Opt. Precision Eng., 2016, 24(4): 873-881. (in Chinese)