激光技术, 2020, 44 (4): 485, 网络出版: 2020-07-16   

3维卷积递归神经网络的高光谱图像分类方法

Hyperspectral image classification based on 3-D convolutional recurrent neural network
作者单位
空军航空大学,长春 130022
引用该论文

关世豪, 杨桄, 李豪, 付严宇. 3维卷积递归神经网络的高光谱图像分类方法[J]. 激光技术, 2020, 44(4): 485.

GUAN Shihao, YANG Guang, LI Hao, FU Yanyu. Hyperspectral image classification based on 3-D convolutional recurrent neural network[J]. Laser Technology, 2020, 44(4): 485.

参考文献

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[15] ZHANG B. Hyperspectral image classification and target detection[M]. 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).

关世豪, 杨桄, 李豪, 付严宇. 3维卷积递归神经网络的高光谱图像分类方法[J]. 激光技术, 2020, 44(4): 485. GUAN Shihao, YANG Guang, LI Hao, FU Yanyu. Hyperspectral image classification based on 3-D convolutional recurrent neural network[J]. Laser Technology, 2020, 44(4): 485.

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