[1] BIOUCAS-DIAS J M, PLAZA A, CAMPS-VALLS G, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2):6-36. doi: 10.1109/MGRS.2013.2244672
[2] DALE L M, THEWIS A, BOUDRY C, et al. Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review[J]. Applied Spectroscopy Reviews, 2013, 48(2):142-159. doi: 10.1080/05704928.2012.705800
[3] GHIYAMAT A, SHAFRI H Z M. A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment[J]. International Journal of Remote Sensing, 2010, 31(7):1837-1856. doi: 10.1080/01431160902926681
[4] van der MEER F D, van dwe WERFF H M A, van RUITENBEEK F J A, et al. Multi- and hyperspectral geologic remote sensing: A review[J]. International Journal of Applied Earth Observation & Geoinformation, 2012, 14(1):112-128.
[5] ELIZABETH A W, SHAROLYN A, MICHAIL F, et al. Supporting global environmental change research: A review of trends and know-ledge gaps in urban remote sensing[J]. Remote Sensing, 2014, 6(5):3879-3905. doi: 10.3390/rs6053879
[6] YUEN P W, RICHARDSON M. An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition[J]. The Imaging Science Journal, 2010, 58(5):241-253. doi: 10.1179/174313110X12771950995716
[7] PILORGET C, BIBRING J P. Automated algorithms to identify and locate grains of specific composition for NIR hyperspectral microscopes: Application to the micromega instrument onboard exomars[J]. Planetary and Space Science, 2014, 99:7-18. doi: 10.1016/j.pss.2014.05.017
[8] HU W, HUANG Y Y, WEI L, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015(10): 1-12.
[9] YANG J, ZHAO Y Q, CHAN C W. Learning and transferring deep joint spectral-spatial features for hyperspectral classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(8):4729-4742. doi: 10.1109/TGRS.2017.2698503
[10] HE M, LI B, CHEN H. Multi-scale 3-D deep convolutional neural network for hyperspectral image classification[C]//2017 IEEE International Conference on Image Processing (ICIP). New York, USA: IEEE, 2017: 57-61.
[11] LI G D, ZHANG Ch J, GAO F, et al. Doubleconvpool-structured 3D-CNN for hyperspectral remote sensing image classification[J]. Journal of Image and Graphics, 2019, 24(4): 639-654(in Chin-ese).
[12] WU H, SAURABH P. Convolutional recurrent neural networks for hyperspectral data classification[J]. Remote Sensing, 2017, 9(3): 298-303. doi: 10.3390/rs9030298
[13] MOU L, GHAMISI P, ZHU X X. Unsupervised spectral-spatial feature learning via deep residual conv-deconv network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(1):391-406. doi: 10.1109/TGRS.2017.2748160
[14] MOU L, GHAMISI P, ZHU X X. Deep recurrent neural networks for hyperspectral image classification[J]. IEEE Transaction Geoscience and Remote Sensing, 2017, 55(7):3639-3655. doi: 10.1109/TGRS.2016.2636241
[15] ZHANG B. Hyperspectral image classification and target detection. Beijing: Science Press, 2011: 9-10(in Chinese).
[16] DU P J, XIA J Sh, XUE Zh H, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016, 20(2): 236-256(in Chinese).
[17] QI Y F, MA Zh Y. Hyperspectral image classification method based on neighborhood speetra and probability cooperative representation[J].Laser Technology, 2019, 43(4):448-452(in Chinese).
[18] ZHANG H K, LI Y, JIANG Y N. Deep learning for hyperspectral imagery classification: The state of the art and prospects[J]. Acta Automatia Sinica, 2018, 44(6): 961-977(in Chinese).
[19] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014(9):34-37.
[20] LIU J. Hyperspectral image classification based on long short term memory network[D]. Xi'an: Xidian University, 2018: 19-21(in Chinese).