2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

深度学习在高光谱图像分类领域的研究现状与展望

张号逵 李映 姜晔楠

张号逵, 李映, 姜晔楠. 深度学习在高光谱图像分类领域的研究现状与展望. 自动化学报, 2018, 44(6): 961-977. doi: 10.16383/j.aas.2018.c170190
引用本文: 张号逵, 李映, 姜晔楠. 深度学习在高光谱图像分类领域的研究现状与展望. 自动化学报, 2018, 44(6): 961-977. doi: 10.16383/j.aas.2018.c170190
ZHANG Hao-Kui, LI Ying, JIANG Ye-Nan. Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects. ACTA AUTOMATICA SINICA, 2018, 44(6): 961-977. doi: 10.16383/j.aas.2018.c170190
Citation: ZHANG Hao-Kui, LI Ying, JIANG Ye-Nan. Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects. ACTA AUTOMATICA SINICA, 2018, 44(6): 961-977. doi: 10.16383/j.aas.2018.c170190

深度学习在高光谱图像分类领域的研究现状与展望

doi: 10.16383/j.aas.2018.c170190
基金项目: 

陕西省国际科技合作计划项目 2017KW-006

西北工业大学博士论文创新基金 CX201816

国家重点研发计划项目 2016YFB0502502

预研领域基金课题 614023804016HK03002

详细信息
    作者简介:

    张号逵 西北工业大学计算机学院陕西省语音与图像信息处理重点实验室博士研究生.2016年于西北工业大学计算机学院陕西省语音与图像信息处理重点实验室获得计算机应用技术专业硕士学位.主要研究方向为图像处理, 深度学习, 高光谱图像分类.E-mail:hkzhang1991@mail.nwpu.edu.cn

    姜晔楠 西北工业大学计算机学院陕西省语音与图像信息处理重点实验室博士研究生.2017年于东北林业大学获得计算机科学与技术专业硕士学位.主要研究方向为高光谱图像分类, 深度学习.E-mail:ynjiang@mail.nwpu.edu.cn

    通讯作者:

    李映 西北工业大学计算机学院教授.2002年于西安电子科技大学雷达信号处理国家重点实验室电路与系统专业获博士学位.主要研究方向为图像处理, 计算智能, 信号处理.本文通信作者.E-mail:lybyp@nwpu.edu.cn

Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects

Funds: 

Shaanxi International Scientific and Technological Cooperation Project 2017KW-006

Sponsored by Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University CX201816

National Key Research and Development Program 2016YFB0502502

Foundation Project for Advanced Research Field 614023804016HK03002

More Information
    Author Bio:

    Ph. D. candidate at Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, the School of Computer Science, Northwestern Polytechnical University. He received his master degree in computer application technology from Shaanxi Provincial Key Laboratory of Speech and Image Information Processing in 2016. His research interest covers image processing, deep learning, and hyperspectral image classification

    Ph. D. candidate at Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, the School of Computer Science, Northwestern Polytechnical University. She received her master degree in computer science from Northeast Forestry University in 2017. Her research interest covers hyperspectral image processing and deep learning

    Corresponding author: LI Ying Professor at the School of Computer Science. She received her Ph. D. degree in electrical circuit and system, from the National Key Laboratory of Radar Signal Processing, Xidian University in 2002. Her research interest covers image processing, computation intelligence, and signal processing. Corresponding author of this paper
  • 摘要: 高光谱图像(Hyperspectral imagery,HSI)分类是高光谱遥感对地观测技术的一项重要内容,在军事及民用领域都有着重要的应用.然而,高光谱图像的高维特性、波段间高度相关性、光谱混合等使得高光谱图像分类面临巨大挑战.近年来,随着深度学习新技术的出现,基于深度学习的高光谱图像分类在方法和性能上得到了突破性的进展,为其研究提供了新的契机.本文首先介绍了高光谱图像分类的背景、研究现状及几个常用的数据集,并简要概述了几种典型的深度学习模型,最后详细介绍了当前的一些基于深度学习的高光谱图像分类方法,总结了深度学习在高光谱图像分类领域中的主要作用和存在的问题,并对未来的研究方向进行了展望.
    1)  本文责任编委 张军平
  • 图  1  高光谱图像示意图

    Fig.  1  Illustration of HSI

    图  2  CNN网络结构

    Fig.  2  The structure of CNN

    图  3  SAE网络结构

    Fig.  3  The structure of SAE

    图  4  DBN网络结构

    Fig.  4  The structure of DBN

    图  5  基于1D-CNN的高光谱图像谱特征分类

    Fig.  5  Spectral feature classification of HSI based on 1D-CNN

    图  6  基于2D-CNN的高光谱图像谱特征分类

    Fig.  6  Spatial feature classification of HSI based on 2D-CNN

    图  7  基于谱特征和空间特征分别提取的分类流程图

    Fig.  7  The flow chart of classification based on extracting spectral and spatial feature separately

    图  8  基于3D-CNN同时提取谱特征和空间特征的分类流程图

    Fig.  8  The flow chart of classification based on 3D-CNN extracting spectral and spatial feature simultaneously

    图  9  二维卷积和三维卷积

    Fig.  9  2D convolution operation and 3D convolution operation

    图  10  基于SAE的高光谱图像分类

    Fig.  10  HSI classification based on SAE

    图  11  去噪自编码示意图

    Fig.  11  The illustration of dAE

    图  12  基于DBN的高光谱图像分类

    Fig.  12  HSI classification based on DBN

    图  13  Indian Pines分类结果图

    Fig.  13  The classification of Indian Pines

    表  1  高光谱图像分类常用数据集

    Table  1  Several common datasets of HSI classification

    数据Indian PinesSalinasKennedy Space CenterPavia CenterPavia UniversityBotswana
    采集时间1992年1992年1996年2001年2001年2001年
    采集地点印第安纳州加利福尼亚州佛罗里达意大利北部意大利北部奥卡万戈三角洲
    采集设备AVIRISAVIRISAVIRISROSISROSISHyperion
    光谱覆盖范围($\mu$m)0.4 $\times$ 2.50.4 $\times$ 2.50.4 $\times$ 2.50.43 $\times$ 0.860.43 $\times$ 0.860.4 $\times$ 2.5
    数据大小(像素)145 $\times$ 145512 $\times$ 217512 $\times$ 6141 096 $\times$ 492610 $\times$ 3401 476 $\times$ 256
    空间分辨率(m)203.7181.31.330
    波段数224224224115115242
    去噪后波段数200204176102103145
    样本量10 24954 1295 2117 45642 7763 248
    类别数1616139914
    下载: 导出CSV

    表  2  几种主流的深度学习开发工具

    Table  2  Several mainstream development tools of deep learning

    工具机构支持语言官网
    TensorflowGooglePython/C++/Go/Javahttps://www.tensorflow.org/
    TheanoU MontrealPythonhttp://deeplearning.net/software/theano/
    PytorchFacebookPythonhttp://pytorch.org/
    CaffeBVLCC++/Python/Matlabhttp://caffe.berkeleyvision.org/
    CNTKMicrosoftC++/Python/Chttp://cntk.codeplex.com/
    Matconvnet/Matlabhttp://www.vlfeat.org/matconvnet/
    MXNetDMLCPython/C++/R/Julia/Scala/Go/Matlab/JavaScripthttp://mxnet.io/index.html
    TorchFacebookLuahttp://torch.ch/
    Deeplearning4JDeepLearning4JJava/Scalahttps://deeplearning4j.org/
    下载: 导出CSV

    表  3  Pavia University分类结果

    Table  3  The classification results of Pavia University

    模型谱特征空间特征空谱联合特征
    OA (%)AA (%)K ($\times$ 100)OA (%)AA (%)K ($\times$ 100)OA (%)AA (%)K ($\times$ 100)
    CNN[71]92.2892.5590.3794.0497.5292.4399.5499.7799.56
    SAE[31]95.1494.0193.7098.1297.3297.5598.5297.8298.07
    DBN[32]96.4295.0995.3098.6297.9598.1999.0598.4898.75
    下载: 导出CSV

    表  4  Indian Pines分类结果

    Table  4  The classification results of Indian Pines

    模型SAE[31]DBN[32]2D-CNN[64]3D-CNN[76]DC-CNN[71]
    OA (%)93.9895.9195.9799.0799.92
    AA (%)93.8194.2093.2398.6699.57
    K ($\times$ 100)93.1395.3495.4098.9399.91
    下载: 导出CSV
  • [1] Bioucas-Dias J M, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N, Chanussot J. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2):6-36 doi: 10.1109/MGRS.2013.2244672
    [2] Benediktsson J A, Chanussot J, Moon W M. Very high-resolution remote sensing:challenges and opportunities[point of view]. Proceedings of the IEEE, 2012, 100(6):1907-1910 doi: 10.1109/JPROC.2012.2190811
    [3] Fauvel M, Tarabalka Y, Benediktsson J A, Chanussot J, Tilton J C. Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE, 2013, 101(3):652-675 doi: 10.1109/JPROC.2012.2197589
    [4] Landgrebe D. Hyperspectral image data analysis. IEEE Signal Processing Magazine, 2002, 19(1):17-28 doi: 10.1109/79.974718
    [5] Nasrabadi N M. Hyperspectral target detection:an overview of current and future challenges. IEEE Signal Processing Magazine, 2014, 31(1):34-44 doi: 10.1109/MSP.2013.2278992
    [6] Van der Meer F. Analysis of spectral absorption features in hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 2004, 5(1):55-68 doi: 10.1016/j.jag.2003.09.001
    [7] Hege E K, O'Connell D, Johnson W, Basty S, Dereniak E L. Hyperspectral imaging for astronomy and space surviellance. In: Proceedings of the 2004 Volume 5159, Imaging Spectrometry IX. San Diego, USA: SPIE, 2004. 380-391
    [8] Gowen A A, O'Donnell C P, Cullen P J, Downey G, Frias J M. Hyperspectral imaging-an emerging process analytical tool for food quality and safety control. Trends Food Science & Technology, 2007, 18(12):590-598 http://www.academia.edu/10343624/Hyperspectral_imaging_-_an_emerging_process_analytical_tool_for_food_quality_and_safety_control
    [9] Lacar F M, Lewis M M, Grierson I T. Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia. In: Proceedings of the 2001 IEEE International Geoscience and Remote Sensing Symposium. Sydney, Australia: IEEE, 2001. 2875-2877 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=978191
    [10] Stuffler T, Förster K, Hofer S, Leipold M, Sang B, Kaufmann H et.al. Hyperspectral imaging-An advanced instrument concept for the EnMAP mission (environmental mapping and analysis programme). Acta Astronautica, 2009, 65(7-8):1107-1112 doi: 10.1016/j.actaastro.2009.03.042
    [11] Malthus T J, Mumby P J. Remote sensing of the coastal zone:an overview and priorities for future research. International Journal of Remote Sensing, 2003, 24(13):2805-2815 doi: 10.1080/0143116031000066954
    [12] Plaza A, Du Q, Chang Y L, King R L. High performance computing for hyperspectral remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 4(3):528-544 doi: 10.1109/JSTARS.2010.2095495
    [13] Plaza A, Martinez P, Perez R, Plaza J. A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles. Pattern Recognition, 2004, 37(6):1097-1116 doi: 10.1016/j.patcog.2004.01.006
    [14] Samat A, Li J, Liu S C, Du P J, Miao Z L, Luo J Q. Improved hyperspectral image classification by active learning using pre-designed mixed pixels. Pattern Recognition, 2016, 51:43-58 doi: 10.1016/j.patcog.2015.08.019
    [15] Imani M, Ghassemian H. Band clustering-based feature extraction for classification of hyperspectral images using limited training samples. IEEE Geoscience and Remote Sensing Letters, 2014, 11(8):1325-1329 doi: 10.1109/LGRS.2013.2292892
    [16] Li F, Xu L L, Siva P, Wong A, Clausi A. Hyperspectral image classification with limited labeled training samples using enhanced ensemble learning and conditional random fields. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6):2427-2438 doi: 10.1109/JSTARS.2015.2414816
    [17] Kumar S, Ghosh J, Crawford M M. Best-bases feature extraction algorithms for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(7):1368-1379 doi: 10.1109/36.934070
    [18] Samaniego L, Bardossy A, Schulz K. Supervised classification of remotely sensed imagery using a modified k-NN technique. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(7):2112-2125 doi: 10.1109/TGRS.2008.916629
    [19] Ediriwickrema J, Khorram S. Hierarchical maximum-likelihood classification for improved accuracies. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4):810-816 doi: 10.1109/36.602523
    [20] Foody G M, Mathur A. A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(6):1335-1343 doi: 10.1109/TGRS.2004.827257
    [21] Hughes G. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 1968, 14(1):55-63 doi: 10.1109/TIT.1968.1054102
    [22] Villa A, Benediktsson J A, Chanussot J, Jutten C. Hyperspectral image classification with independent component discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(12):4865-4876 doi: 10.1109/TGRS.2011.2153861
    [23] Prasad S, Bruce L M. Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4):625-629 doi: 10.1109/LGRS.2008.2001282
    [24] Li W, Prasad S, Fowler J E, Bruce L M. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4):1185-1198 doi: 10.1109/TGRS.2011.2165957
    [25] Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8):1778-1790 doi: 10.1109/TGRS.2004.831865
    [26] Li J, Bioucas-Dis J M, Plaza A. Hyperspectral image segmentation using a new Bayesian approach with active learning. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10):3947-3960 doi: 10.1109/TGRS.2011.2128330
    [27] Liao W Z, Pizurica A, Scheunders P, Philips W, Pi Y G. Semisupervised local discriminant analysis for feature extraction in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1):184-198 doi: 10.1109/TGRS.2012.2200106
    [28] Li W, Prasad S, Fowler J E, Bruce L M. Locality-preserving discriminant analysis in kernel-induced feature spaces for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2011, 8(5):894-898 doi: 10.1109/LGRS.2011.2128854
    [29] Zhong Y F, Zhang L P. An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3):894-909 doi: 10.1109/TGRS.2011.2162589
    [30] Sun L, Wu Z B, Liu J J, Xiao L, Wei Z H. Supervised spectral-spatial hyperspectral image classification with weighted Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3):1490-1503 doi: 10.1109/TGRS.2014.2344442
    [31] Chen Y S, Lin Z H, Zhao X, Wang G, Gu Y F. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6):2094-2107 doi: 10.1109/JSTARS.2014.2329330
    [32] Chen Y S, Zhao X, Jia X P. Spectral-spatial classification of hyperspectral data based on deep belief network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6):2381-2392 doi: 10.1109/JSTARS.2015.2388577
    [33] Zhang L P, Zhang L F, Tao D C, Huang X. Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1):242-256 doi: 10.1109/TGRS.2012.2197860
    [34] Zhang L F, Zhang Q, Zhang L P, Tao D C, Huang X, Du B. Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recognition, 2015, 48(10):3102-3112 doi: 10.1016/j.patcog.2014.12.016
    [35] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7):1527-1554 doi: 10.1162/neco.2006.18.7.1527
    [36] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA: Curran Associates, 2012. 1097-1105 http://dl.acm.org/citation.cfm?id=2999257
    [37] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786):504-507 doi: 10.1126/science.1127647
    [38] Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization[Online], available: http://arxiv.org/abs/1409.2329, July 7, 2017.
    [39] 朱煜, 赵江坤, 王逸宁, 郑兵兵.基于深度学习的人体行为识别算法综述.自动化学报, 2016, 42(6):848-857 http://www.aas.net.cn/CN/abstract/abstract18875.shtml

    Zhu Yu, Zhao Jiang-Kun, Wang Yi-Ning, Zheng Bing-Bing. A review of human action recognition based on deep learning. Acta Automatica Sinica, 2016, 42(6):848-857 http://www.aas.net.cn/CN/abstract/abstract18875.shtml
    [40] He K M, Zhang X Y, Ren S Q, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916 doi: 10.1109/TPAMI.2015.2389824
    [41] 罗建豪, 吴建鑫.基于深度卷积特征的细粒度图像分类研究综述.自动化学报, 2017, 43(8):1306-1318 http://www.aas.net.cn/CN/abstract/abstract19105.shtml

    Luo Jian-Hao, Wu Jian-Xin. A survey on fine-grained image categorization using deep convolutional features. Acta Automatica Sinica, 2017, 43(8):1306-1318 http://www.aas.net.cn/CN/abstract/abstract19105.shtml
    [42] Girshick R. Fast R-CNN. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 1440-1448
    [43] Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, USA: IEEE, 2014. 580-587
    [44] 管皓, 薛向阳, 安志勇.深度学习在视频目标跟踪中的应用进展与展望.自动化学报, 2016, 42(6):834-847 http://www.aas.net.cn/CN/abstract/abstract18874.shtml

    Guan Hao, Xue Xiang-Yang, An Zhi-Yong. Advances on application of deep learning for video object tracking. Acta Automatica Sinica, 2016, 42(6):834-847 http://www.aas.net.cn/CN/abstract/abstract18874.shtml
    [45] Liu F Y, Shen C H, Lin G S. Deep convolutional neural fields for depth estimation from a single image. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015. 5162-5170 doi: 10.1109/CVPR.2015.7299152
    [46] Dong C, Chen C L, He K M, Tang X O. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2):295-307 doi: 10.1109/TPAMI.2015.2439281
    [47] 孙旭, 李晓光, 李嘉锋, 卓力.基于深度学习的图像超分辨率复原研究进展.自动化学报, 2017, 43(5):697-709 http://www.aas.net.cn/CN/abstract/abstract19048.shtml

    Sun Xu, Li Xiao-Guang, Li Jia-Feng, Zhuo Li. Review on deep learning based image super-resolution restoration algorithms. Acta Automatica Sinica, 2017, 43(5):697-709 http://www.aas.net.cn/CN/abstract/abstract19048.shtml
    [48] Lin M, Chen Q, Yang S C. Network in network[Online], available: https://arxiv.org/abs/1312.4400, July 7, 2017
    [49] Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015. 1-9 http://cn.bing.com/academic/profile?id=9502979ad980c7103eca9034a162b820&encoded=0&v=paper_preview&mkt=zh-cn
    [50] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[Online], available: https://arxiv.org/abs/1409.1556, July 7, 2017
    [51] He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE, 2016. 770-778 doi: 10.1109/CVPR.2016.90
    [52] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015. 3431-3440
    [53] Wan L, Zeiler M, Zhang S X, LeCun Y, Fergus R. Regularization of neural networks using DropConnect. In: Proceeding of the 30th International Conference on Machine Learning. Atlanta, USA: IMLS, 2013. 1058-1066
    [54] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceeding of the 32th International Conference on Machine Learning. Lille, France: IMLS, 2015. 448-456 http://dl.acm.org/citation.cfm?id=3045167
    [55] Erhan D, Bengio Y, Courville A, Manzagol P, Vincent P, Bengio S. Why does unsupervised pre-training help deep learning. Journal of Machine Learning Research, 2010, 11:625-660 http://cn.bing.com/academic/profile?id=e9e036a8fb2655e750794481e527b32f&encoded=0&v=paper_preview&mkt=zh-cn
    [56] Hinton G E. Training products of experts by minimizing contrastive divergence. Neural Computation, 2002, 14(8):1771-1800 doi: 10.1162/089976602760128018
    [57] Li J, Bioucas-Dias J M, Plaza A. Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(11):4085-4098 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.1516
    [58] Hu W, Huang Y Y, Wei L, Zhang F, Li H C. Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015, 2015: Article No. 258619 http://www.tandfonline.com/servlet/linkout?suffix=CIT0026&dbid=16&doi=10.1080%2F15481603.2018.1426091&key=10.1155%2F2015%2F258619
    [59] Liao J G, Chin K V. Logistic regression for disease classification using microarray data:model selection in a large p and small n case. Bioinformatics, 2007, 23(15):1945-1951 doi: 10.1093/bioinformatics/btm287
    [60] Mei S H, Ji J Y, Bi Q Q, Hou J H, Du Q, Li W. Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification. In: Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China: IEEE, 2016. 5067-5070 http://ieeexplore.ieee.org/document/7730321/
    [61] He K M, Zhang X Y, Ren S Q, Sun J. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 1026-1034 doi: 10.1109/ICCV.2015.123
    [62] Tang Y Y, Lu Y, Yuan H L. Hyperspectral image classification based on three-dimensional scattering wavelet transform. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5):2467-2480 doi: 10.1109/TGRS.2014.2360672
    [63] Makantasis K, Karantzalos K, Doulamis A, Doulamis N. Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan, Italy: IEEE, 2015. 4959-4962 doi: 10.1109/IGARSS.2015.7326945
    [64] Yue J, Zhao W Z, Mao S J, Liu H. Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sensing Letters, 2015, 6(6):468-477 doi: 10.1080/2150704X.2015.1047045
    [65] Zhao W Z, Guo Z, Yue J, Zhang X Y, Luo L Q. On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery. International Journal of Remote Sensing, 2015, 36(13):3368-3379 doi: 10.1080/2150704X.2015.1062157
    [66] Aptoula E, Ozdemir M C, Yanikoglu B. Deep learning with attribute profiles for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12):1970-1974 doi: 10.1109/LGRS.2016.2619354
    [67] Dalla Mura M, Benediktsson J A, Waske B, Bruzzone L. Morphological attribute profiles for the analysis of very high resolution images. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10):3747-3762 doi: 10.1109/TGRS.2010.2048116
    [68] Liang H M, Li Q. Hyperspectral imagery classification using sparse representations of convolutional neural network features. Remote Sensing, 2016, 8(2):Article No.99 http://cn.bing.com/academic/profile?id=075fc2963b6ad47a56b59f2314cbf4b5&encoded=0&v=paper_preview&mkt=zh-cn
    [69] Li Y S, Xie W Y, Li H Q. Hyperspectral image reconstruction by deep convolutional neural network for classification. Pattern Recognition, 2017, 63:371-383 doi: 10.1016/j.patcog.2016.10.019
    [70] Yang J X, Zhao Y Q, Chan J C W, Yi C. Hyperspectral image classification using two-channel deep convolutional neural network. In: Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China: IEEE, 2016. 5079-5082 http://ieeexplore.ieee.org/abstract/document/7730324/
    [71] Zhang H K, Li Y, Zhang Y Z, Shen Q. Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network. Remote Sensing Letters, 2017, 8(5):438-447 doi: 10.1080/2150704X.2017.1280200
    [72] Yue J, Mao S J, Li M. A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sensing Letters, 2016, 7(9):875-884 doi: 10.1080/2150704X.2016.1193793
    [73] Zhao W Z, Du S H. Spectral-spatial feature extraction for hyperspectral image classification:a dimension reduction and deep learning approach. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4544-4554 doi: 10.1109/TGRS.2016.2543748
    [74] Chen H T, Chang H W, Liu T L. Local discriminant embedding and its variants. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 846-853 http://dl.acm.org/citation.cfm?id=1069174
    [75] Chen Y S, Jiang H L, Li C Y, Jia X P, Ghamisi P. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10):6232-6251 doi: 10.1109/TGRS.2016.2584107
    [76] Li Y, Zhang H K, Shen Q. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sensing, 2017, 9(1):Article No.67 doi: 10.3390/rs9010067
    [77] Lee H, Kwon H. Contextual deep CNN based hyperspectral classification. In: Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE, 2016. 3322-3325 http://ieeexplore.ieee.org/document/7729859/
    [78] Slavkovikj V, Verstockt S, De Neve W, Van Hoecke S, Van de Walle R. Hyperspectral image classification with convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia. Brisbane, Australia: ACM, 2015. 1159-1162 http://dl.acm.org/citation.cfm?id=2806306
    [79] Lin Z H, Chen Y S, Zhao X, Wang G. Spectral-spatial classification of hyperspectral image using autoencoders. In: Proceedings of the 9th International Conference on Information, Communications and Signal Processing (ICICS). Tainan, China: IEEE, 2013. 1-5
    [80] Ma X R, Wang H Y, Geng J, Wang J. Hyperspectral image classification with small training set by deep network and relative distance prior. In: Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China: IEEE, 2016. 3282-3285 http://ieeexplore.ieee.org/document/7729849/
    [81] Xing C, Ma L, Yang X Q. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images. Journal of Sensors, 2016, 2016:Article No.3632943 http://cn.bing.com/academic/profile?id=25e02bbd78f42e5decaf769b42c58c25&encoded=0&v=paper_preview&mkt=zh-cn
    [82] Liu Y Z, Cao G, Sun Q S, Siegel M. Hyperspectral classification via deep networks and superpixel segmentation. International Journal of Remote Sensing, 2015, 36(13):3459-3482 doi: 10.1080/01431161.2015.1055607
    [83] Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P A. Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11:3371-3408 http://cn.bing.com/academic/profile?id=1b5321cd3b5fdc0bc4d9b0d4fa8975ef&encoded=0&v=paper_preview&mkt=zh-cn
    [84] Zhang L P, Zhang L F, Du B. Deep learning for remote sensing data:a technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2):22-40 doi: 10.1109/MGRS.2016.2540798
    [85] Ma X R, Wang H Y, Geng J. Spectral-spatial classification of hyperspectral image based on deep auto-encoder. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9):4073-4085 doi: 10.1109/JSTARS.2016.2517204
    [86] Tao C, Pan H B, Li Y S, Zou Z R. Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12):2438-2442 doi: 10.1109/LGRS.2015.2482520
    [87] Shin H C, Orton M R, Collins D J, Doran S J, Leach M O. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8):1930-1943 doi: 10.1109/TPAMI.2012.277
    [88] Wang L Z, Zhang J B, Liu P, Choo K K R, Huang F. Spectral-spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Computing, 2017, 21(1):213-221 doi: 10.1007/s00500-016-2246-3
    [89] Li J M, Bruzzone L, Liu S C. Deep feature representation for hyperspectral image classification. In: Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan, Italy: IEEE, 2015. 4951-4954 http://ieeexplore.ieee.org/document/7326943/
    [90] Ma X R, Geng J, Wang H Y. Hyperspectral image classification via contextual deep learning. EURASIP Journal on Image and Video Processing, 2015, 2015(1):Article No.20 doi: 10.1186/s13640-015-0071-8
    [91] Han X B, Zhong Y F, Zhang L P. Spatial-spectral classification based on the unsupervised convolutional sparse auto-encoder for hyperspectral remote sensing imagery. In: Proceedings of the 2016 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic: ISPRS, 2016. 25-31 http://adsabs.harvard.edu/abs/2016ISPAnIII7...25H
    [92] Ma X R, Wang H Y, Wang J. Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 120:99-107 doi: 10.1016/j.isprsjprs.2016.09.001
    [93] He M Y, Li X H, Zhang Y F, Zhang J, Wang W G. Hyperspectral image classification based on deep stacking network. In: Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China: IEEE, 2016. 3286-3289 http://ieeexplore.ieee.org/document/7729850/
    [94] Zhong P, Gong Z Q, Schönlieb C. A diversified deep belief network for hyperspectral image classification. In: Proceedings of the 2016 ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic: ISPRS, 2016. 443-449 http://adsabs.harvard.edu/abs/2016ISPAr41B7..443Z
    [95] Li T, Zhang J P, Zhang Y. Classification of hyperspectral image based on deep belief networks. In: Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP). Paris, France: IEEE, 2014. 5132-5136 http://ieeexplore.ieee.org/document/7026039/
    [96] Le J H, Yazdanpanah A P, Regentova E, Muthukumar V. A deep belief network for classifying remotely-sensed hyperspectral data. In: Proceedings of the 11th International Symposium on Visual Computing. Las Vegas, USA: Springer, 2015. 682-692 doi: 10.1007%2F978-3-319-27857-5_61
    [97] Thompson W D, Walter S D. A reappraisal of the kappa coefficient. Journal of Clinical Epidemiology, 1988, 41(10):949-958 doi: 10.1016/0895-4356(88)90031-5
    [98] Vinyals O, Blundell C, Lillicrap T, Kavukcuoglu K, Wierstra D. Matching networks for one short learning. In: Proceedings of the 2016 Advances in Neural Information Processing Systems. Barcelona, Spain: Curran Associates, 2016. 3630-3638
    [99] Socher R, Ganjoo M, Sridhar H, Bastani O, Manning C D, Ng A Y. Zero-short learning through cross-modal transfer. In: Proceedings of the 2013 Advances in Neural Information Processing Systems. Lake Tahoe, USA: Curran Associates, 2013. 935-943 http://arxiv.org/abs/1301.3666
    [100] Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: Curran Associates, 2014. 2672-2680
    [101] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention. Munich, Germany: Springer, 2015. 234-241
  • 加载中
图(13) / 表(4)
计量
  • 文章访问数:  6420
  • HTML全文浏览量:  2000
  • PDF下载量:  2028
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-04-14
  • 录用日期:  2017-09-23
  • 刊出日期:  2018-06-20

目录

    /

    返回文章
    返回