高光谱遥感影像半监督分类研究进展

杨星; 方乐缘; 岳俊

doi:10.11834/jrs.20243404

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高光谱遥感影像半监督分类研究进展
Advances in semi-supervised classification of hyperspectral remote sensing images
2024年28卷第1期页码：20-41
纸质出版日期： 2024-01-07 ，
DOI： 10.11834/jrs.20243404
稿件说明：

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杨星，方乐缘，岳俊.2024.高光谱遥感影像半监督分类研究进展.遥感学报，28（1）： 20-41

Yang X，Fang L Y and Yue J. 2024. Advances in semi-supervised classification of hyperspectral remote sensing images. National Remote Sensing Bulletin， 28（1）：20-41
杨星，方乐缘，岳俊.2024.高光谱遥感影像半监督分类研究进展.遥感学报，28（1）： 20-41 DOI： 10.11834/jrs.20243404.

Yang X，Fang L Y and Yue J. 2024. Advances in semi-supervised classification of hyperspectral remote sensing images. National Remote Sensing Bulletin， 28（1）：20-41 DOI： 10.11834/jrs.20243404.

摘要

随着高光谱遥感技术的迅猛发展和应用需求的不断增加，高光谱遥感影像分类成为领域的研究热点。尽管监督学习已在高光谱遥感影像分类中取得了不错的效果，但在许多情况下，获取大规模标记样本来训练监督分类算法是困难和昂贵的。因此，利用半监督分类技术对高光谱遥感影像精准分类是一项重要的研究内容。本文首先简要介绍了高光谱遥感影像发展现状和部分应用场景。其次，本文对近年来高光谱遥感影像半监督分类研究的进展进行了综述，着重讨论了低密度分割法、生成式模型、基于分歧（差异）的方法和基于图的方法四种典型半监督分类方法的关键技术和优劣。最后，进一步讨论了半监督分类技术的潜力，为今后研究工作的优化提供思路。

Abstract

Hyperspectral remote sensing technology has been widely used in remote sensing

agriculture

geological exploration

and other fields

and hyperspectral image classification is one of the most important research directions. Benefiting from sufficient label information

supervised learning has achieved good results in this field. However

in many practical applications of hyperspectral remote sensing images

sufficient label samples are difficult to obtain. One of the most important reasons is the widespread use of hyperspectral remote sensing technology

which produces huge amounts of unlabeled data. Another is the high cost of labeling. Meanwhile

unsupervised learning cannot accurately cluster unknown data

and its clustering categories are to match to real categories. Both supervised and unsupervised learning have their unavoidable disadvantages. Therefore

semi-supervised learning that uses a large number of unlabeled samples and a small number of labeled samples should be explored. In recent years

significant progress has been made in the semi supervised classification of hyperspectral remote sensing images. Researchers have proposed many innovative algorithms and technologies to address the problem of insufficient data annotation. This article reviews the progress of the semi supervised classification research on hyperspectral remote sensing images in recent years

discussing key technologies and methods.

This paper starts with semi-supervised classification and hyperspectral remote sensing technologies. First

the first part of this paper introduces some basic concepts of semi-supervised learning

including semi-supervised and unsupervised learning

supervised learning

and the application of semi-supervised learning. The second part introduces the development of hyperspectral remote sensing imaging technology domestically and internationally and the application of hyperspectral remote sensing in various fields

such as land and resource surveys

agriculture and forestry remote sensing

and urban environmental monitoring. Second

the three basic assumptions of the theory

process

and data distribution of semi-supervised learning are analyzed

and four typical types are introduced: low-density separation

generative

disagreement-based (difference-based)

and graph-based methods. The algorithm flow and core ideas of each method are introduced in detail. The summarized current development status

typical algorithms

and research progress of hyperspectral remote sensing image classification are analyzed. Further

the advantages and disadvantages of each algorithm are enumerated. Then

common open-source algorithms were compared on three publicly available datasets

namely

Indian Pines

Pavia University

and Houston 2013. Finally

by analyzing existing semi-supervised learning technologies and experimental results

the challenging problems and development trends of semi-supervised learning in hyperspectral remote sensing are summarized.

The graph-based semi-supervised classification method performs better than other semi-supervised classification methods

which may be because the graph model can model the relationship and similarity between samples

connect similar samples

and capture the intrinsic structure and similarity in a dataset.

Semi-supervised learning can efficiently utilize both labeled data and unlabeled data. The future development trend of semi-supervised classification is mainly in three aspects: how to effectively use a large number of unlabeled samples; how to fully consider multiple factors

such as performance and computational complexity; and how to select features. These aspects will affect the stability

generalization

practicability

and performance of the algorithm.

关键词

高光谱遥感影像半监督分类低密度分割法生成式模型图神经网络

Keywords

hyperspectral imagesemi-supervised classificationlow-density separationgenerative modelgraph neural network

references

Abdulridha J, Ampatzidis Y, Kakarla S C and Roberts P. 2020. Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precision Agriculture, 21(5): 955-978 [DOI: 10.1007/s11119-019-09703-4http://dx.doi.org/10.1007/s11119-019-09703-4]

Astorino A and Fuduli A. 2015. Semisupervised spherical separation. Applied Mathematical Modelling, 39(20): 6351-6358 [DOI: 10.1016/j.apm.2015.01.044http://dx.doi.org/10.1016/j.apm.2015.01.044]

Bai J, Ding B X, Xiao Z, Jiao L C, Chen H Y and Regan A C. 2022. Hyperspectral image classification based on deep attention graph convolutional network. IEEE Transactions on Geoscience and Remote Sensing, 60(1): 5504316 [DOI: 10.1109/TGRS.2021.3066485http://dx.doi.org/10.1109/TGRS.2021.3066485]

Bai L, Wang J B, Liang J Y and Du H Y. 2020. New label propagation algorithm with pairwise constraints. Pattern Recognition, 106: 107411 [DOI: 10.1016/j.patcog.2020.107411http://dx.doi.org/10.1016/j.patcog.2020.107411]

Bharathi S P, Srinivasan S and Hariharan R. 2022. Improved chimp optimization with deep transfer learning enabled soil classification technique using hyperspectral remote sensing images. Journal of Electronic Imaging, 31(6): 062011 [DOI: 10.1117/1.JEI.31.6.062011http://dx.doi.org/10.1117/1.JEI.31.6.062011]

Bioucas-Dias J M, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N and Chanussot J. 2013. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 1(2): 6-36 [DOI: 10.1109/MGRS.2013.2244672http://dx.doi.org/10.1109/MGRS.2013.2244672]

Blum A, Lafferty J, Rwebangira M R and Reddy R. 2004. Semi-supervised learning using randomized mincuts//Proceedings of the 21st International Conference on Machine Learning. Banff: ACM [DOI: 10.1145/1015330.1015429http://dx.doi.org/10.1145/1015330.1015429]

Blum A and Mitchell T. 1998. Combining labeled and unlabeled data with co-training//Proceedings of the 11th Annual Conference on Computational Learning Theory. Madison: ACM: 92-100 [DOI: 10.1145/279943.279962http://dx.doi.org/10.1145/279943.279962]

Bruna J, Zaremba W, Szlam A and LeCun Y. 2014. Spectral networks and locally connected networks on graphs. arXiv:1312.6203 [DOI: 10.48550/arXiv.1312.6203http://dx.doi.org/10.48550/arXiv.1312.6203]

Bruzzone L, Chi M and Marconcini M. 2006. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3363-3373 [DOI: 10.1109/TGRS.2006.877950http://dx.doi.org/10.1109/TGRS.2006.877950]

Cai J N, Chen J, Dou X H and Xing Q G. 2022a. Using machine learning algorithms with in situ hyperspectral reflectance data to assess comprehensive water quality of urban rivers. IEEE Transactions on Geoscience and Remote Sensing, 60: 5523113 [DOI: 10.1109/TGRS.2022.3147695http://dx.doi.org/10.1109/TGRS.2022.3147695]

Cai R L, Liu C Y and Li J. 2022b. Phase-induced Gabor-based multiview active learning for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 19: 5505005 [DOI: 10.1109/LGRS.2021.3070360http://dx.doi.org/10.1109/LGRS.2021.3070360]

Cai W W and Wei Z G. 2022. Remote sensing image classification based on a cross-attention mechanism and graph convolution. IEEE Geoscience and Remote Sensing Letters, 19: 8002005 [DOI: 10.1109/LGRS.2020.3026587http://dx.doi.org/10.1109/LGRS.2020.3026587]

Cai X L, Wu L Y, Li Y M, Lei S H, Xu J, Lyu H, Li J D, Wang H J, Dong X Z, Zhu Y X and Wang G L. 2023. Remote sensing identification of urban water pollution source types using hyperspectral data. Journal of Hazardous Materials, 459: 132080 [DOI: 10.1016/j.jhazmat.2023.132080http://dx.doi.org/10.1016/j.jhazmat.2023.132080]

Cai Y M, Zhang Z J, Cai Z H, Liu X B and Jiang X W. 2022c. Hypergraph-structured autoencoder for unsupervised and semisupervised classification of hyperspectral image. IEEE Geoscience and Remote Sensing Letters, 19: 5503505 [DOI: 10.1109/LGRS.2021.3054868http://dx.doi.org/10.1109/LGRS.2021.3054868]

Camps-Valls G, Bandos Marsheva T V and Zhou D Y. 2007. Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 45(10): 3044-3054 [DOI: 10.1109/TGRS.2007.895416http://dx.doi.org/10.1109/TGRS.2007.895416]

Cao S B, Liu S T, Shi Y F, Pan Y B, Han L F and Yang Y W. 2022. A semi-supervised support vector machines approach for condition monitoring of construction equipment//2022 IEEE International Conference on Real-time Computing and Robotics (RCAR). Guiyang: IEEE: 192-196 [DOI: 10.1109/RCAR54675.2022.9872264http://dx.doi.org/10.1109/RCAR54675.2022.9872264]

Cavalli R M. 2021. Capability of remote sensing images to distinguish the urban surface materials: a case study of Venice city. Remote Sensing, 13(19): 3959 [DOI: 10.3390/rs13193959http://dx.doi.org/10.3390/rs13193959]

Chaudhri S N, Rajput N S, Singh K P and Singh D. 2019. Different modality based remote sensing data fusion approach for efficient classification of agriculture and urban subclasses//IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 5710-5713 [DOI: 10.1109/IGARSS.2019.8899201http://dx.doi.org/10.1109/IGARSS.2019.8899201]

Chen J, Jiao L C, Liu X, Li L L, Liu F and Yang S Y. 2022a. Automatic graph learning convolutional networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5520716 [DOI: 10.1109/TGRS.2021.3135084http://dx.doi.org/10.1109/TGRS.2021.3135084]

Chen R, Li G H and Dai C L. 2022b. DRGCN: dual residual graph convolutional network for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 19: 6009205 [DOI: 10.1109/LGRS.2022.3171536http://dx.doi.org/10.1109/LGRS.2022.3171536]

Chen W. Ouyang S. Yang J. Li X. Zhou G. & Wang L. 2022c. JAGAN: A Framework for Complex Land Cover Classification Using Gaofen-5 AHSI Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1591-1603. 15.[DOI:10.1109/JSTARS.2022.3144339http://dx.doi.org/10.1109/JSTARS.2022.3144339]

Chen W J, Shao Y H, Deng N Y and Feng Z L. 2014. Laplacian least squares twin support vector machine for semi-supervised classification. Neurocomputing, 145: 465-476 [DOI: 10.1016/j.neucom.2014.05.007http://dx.doi.org/10.1016/j.neucom.2014.05.007]

Crowson M, Hagensieker R and Waske B. 2019. Mapping land cover change in northern Brazil with limited training data. International Journal of Applied Earth Observation and Geoinformation, 78: 202-214 [DOI: 10.1016/j.jag.2018.10.004http://dx.doi.org/10.1016/j.jag.2018.10.004]

Cui Y, Wang X T, Lu Z J and Wang L G. 2018. Hyperspectral image classification based on improved M-training algorithm. Journal of Harbin Engineering University, 39(10): 1688-1694

崔颖, 王雪婷, 陆忠军, 王立国. 2018. 改进M-training算法的高光谱图像分类. 哈尔滨工程大学学报, 39(10): 1688-1694 [DOI: 10.11990/jheu.201707022http://dx.doi.org/10.11990/jheu.201707022]

Cui Y. Song G. Wang X. Lu Z. & Wang L. 2017. Semisupervised classification of hyperspectral images based on tri-training algorithm with enhanced diversity. Journal of Applied Remote Sensing, 11(4), 1-.[DOI: 10.1117/1.JRS.11.045006http://dx.doi.org/10.1117/1.JRS.11.045006]

Darnstädt M, Simon H U and Szörényi B. 2014. Supervised learning and co-training. Theoretical Computer Science, 519: 68-87 [DOI: 10.1016/j.tcs.2013.09.020http://dx.doi.org/10.1016/j.tcs.2013.09.020]

Defferrard M, Bresson X and Vandergheynst P. 2016. Convolutional neural networks on graphs with fast localized spectral filtering//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona: Curran Associates Inc.: 3844-3852

Dempster A P, Laird N M and Rubin D B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1): 1-22 [DOI: 10.1111/j.2517-6161.1977.tb01600.xhttp://dx.doi.org/10.1111/j.2517-6161.1977.tb01600.x]

Ding Y, Zhang Z L, Zhao X F, Cai Y M, Li S Y, Deng B and Cai W W. 2022b. Self-Supervised locality preserving low-pass graph convolutional embedding for large-scale hyperspectral image clustering. IEEE Transactions on Geoscience and Remote Sensing, 60: 5536016 [DOI: 10.1109/TGRS.2022.3198842http://dx.doi.org/10.1109/TGRS.2022.3198842]

Ding Y, Zhao X F, Zhang Z L, Cai W and Yang N J. 2021. Multiscale graph sample and aggregate network with context-aware learning for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 4561-4572 [DOI: 10.1109/JSTARS.2021.3074469http://dx.doi.org/10.1109/JSTARS.2021.3074469]

Ding Y, Zhao X F, Zhang Z L, Cai W, Yang N J and Zhan Y. 2022a. Semi-supervised locality preserving dense graph neural network with ARMA filters and context-aware learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5511812 [DOI: 10.1109/TGRS.2021.3100578http://dx.doi.org/10.1109/TGRS.2021.3100578]

Dong W, Moses C and Li K. 2011. Efficient k-nearest neighbor graph construction for generic similarity measures//Proceedings of the 20th International Conference on World Wide Web. Hyderabad: ACM: 577-586 [DOI: 10.1145/1963405.1963487http://dx.doi.org/10.1145/1963405.1963487]

Dong Y N, Liu Q W, Du B and Zhang L P. 2022. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification. IEEE Transactions on Image Processing, 31: 1559-1572 [DOI: 10.1109/TIP.2022.3144017http://dx.doi.org/10.1109/TIP.2022.3144017]

Dopido I, Li J, Marpu P R, Plaza A, Bioucas Dias J M and Benediktsson J A. 2013. Semisupervised self-learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 51(7): 4032-4044 [DOI: 10.1109/TGRS.2012.2228275http://dx.doi.org/10.1109/TGRS.2012.2228275]

Du J, Ling C X and Zhou Z H. 2011. When does cotraining work in real data?. IEEE Transactions on Knowledge and Data Engineering, 23(5): 788-799 [DOI: 10.1109/TKDE.2010.158http://dx.doi.org/10.1109/TKDE.2010.158]

Du J, Zhang C M, Zhao B C and Sun Y. 2008. Analysis of the transmittance of modified Savart polariscope in the static large field of view polarization interference imaging spectrometer. Acta Physica Sinica, 57(10): 6311-6318

杜娟, 张淳民, 赵葆常, 孙尧. 2008. 稳态大视场偏振干涉成像光谱仪中视场补偿型Savart偏光镜透射率研究. 物理学报, 57(10): 6311-6318 [DOI: 10.3321/j.issn:1000-3290.2008.10.040http://dx.doi.org/10.3321/j.issn:1000-3290.2008.10.040]

Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236-256

杜培军, 夏俊士, 薛朝辉, 谭琨, 苏红军, 鲍蕊. 2016. 高光谱遥感影像分类研究进展. 遥感学报, 20(2): 236-256 [DOI: 10.11834/jrs.20165022http://dx.doi.org/10.11834/jrs.20165022]

Elcoroaristizabal S and Amigo J M. 2021. Near infrared hyperspectral imaging as a tool for quantifying atmospheric carbonaceous aerosol. Microchemical Journal, 160: 105619 [DOI: 10.1016/j.microc.2020.105619http://dx.doi.org/10.1016/j.microc.2020.105619]

Fan M Y, Gu N N, Qiao H and Zhang B. 2011. Sparse regularization for semi-supervised classification. Pattern Recognition, 44(8): 1777-1784 [DOI: 10.1016/j.patcog.2011.02.013http://dx.doi.org/10.1016/j.patcog.2011.02.013]

Fang B, Li Y, Zhang H K and Chan J C W. 2020. Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 161: 164-178 [DOI: 10.1016/j.isprsjprs.2020.01.015http://dx.doi.org/10.1016/j.isprsjprs.2020.01.015]

Fang Z Q, Yang Y X, Li Z K, Li W, Chen Y S, Ma L and Du Q. 2022. Confident Learning-Based Domain Adaptation for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5527116 [DOI: 10.1109/TGRS.2022.3166817http://dx.doi.org/10.1109/TGRS.2022.3166817]

Feng J, Ye Z W, Li D, Liang Y P, Tang X and Zhang X R. 2020. Hyperspectral image classification based on semi-supervised dual-branch convolutional autoencoder with self-attention//IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa: IEEE: 1267-1270 [DOI: 10.1109/IGARSS39084.2020.9323656http://dx.doi.org/10.1109/IGARSS39084.2020.9323656]

Feng J, Zhang J P and Zhang Y. 2022. A multiview spectral-spatial feature extraction and fusion framework for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 19: 5504805 [DOI: 10.1109/LGRS.2021.3066613http://dx.doi.org/10.1109/LGRS.2021.3066613]

Flores C A and Verschae R. 2022. A generic semi-supervised and active learning framework for biomedical text classification//2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Glasgow: IEEE: 4445-4448 [DOI: 10.1109/EMBC48229.2022.9871846http://dx.doi.org/10.1109/EMBC48229.2022.9871846]

Gao Y Z, Chen Y Y, Wang J Q and Lu H Q. 2021. Semi-supervised scene text recognition. IEEE Transactions on Image Processing, 30: 3005-3016 [DOI: 10.1109/TIP.2021.3051485http://dx.doi.org/10.1109/TIP.2021.3051485]

Goetz A F H, Vane G, Solomon J E and Rock B N. 1985. Imaging spectrometry for earth remote sensing. Science, 228(4704): 1147-1153 [DOI: 10.1126/science.228.4704.1147http://dx.doi.org/10.1126/science.228.4704.1147]

Gogna A and Majumdar A. 2016. Semi supervised autoencoder//23rd International Conference on Neural Information Processing. Kyoto: Springer: 82-89 [DOI: 10.1007/978-3-319-46672-9_10http://dx.doi.org/10.1007/978-3-319-46672-9_10]

Gong R. 2018. Gaofen-5 satellite. Satellite Application, (5): 1

龚燃. 2018. 高分五号卫星. 卫星应用, 5: 1 [DOI: 10.3969/j.issn.1674-9030.2018.05.018http://dx.doi.org/10.3969/j.issn.1674-9030.2018.05.018]

Gower J F R, Borstadt G A, Gray L H and Edel H R. 1988. The fluorescence line imager: high-resolution imaging spectroscopy over water and land//Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing. Aussois: [s.n.]: 273-278

Green R O, Eastwood M L, Sarture C M, Chrien T G, Aronsson M, Chippendale B J, Faust J A, Pavri B E, Chovit C J, Solis M, Olah M R and Williams O. 1998. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment, 65(3): 227-248 [DOI: 10.1016/S0034-4257(98)00064-9http://dx.doi.org/10.1016/S0034-4257(98)00064-9]

Grolman E, Cohen D, Frenklach T, Shabtai A and Puzis R. 2022. How and when to stop the co-training process. Expert Systems with Applications, 187: 115841 [DOI: 10.1016/j.eswa.2021.115841http://dx.doi.org/10.1016/j.eswa.2021.115841]

Grotte M E, Birkeland R, Honore-Livermore E, Bakken S, Garrett J L, Prentice E F, Sigernes F, Orlandic M, Gravdahl J T and Johansen T A. 2022. Ocean color hyperspectral remote sensing with high resolution and low latency--The HYPSO-1 CubeSat mission. IEEE Transactions on Geoscience and Remote Sensing, 60: 1000619 [DOI: 10.1109/TGRS.2021.3080175http://dx.doi.org/10.1109/TGRS.2021.3080175]

Guo F M, Li Z W, Xin Z Q, Zhu X, Wang L Q and Zhang J. 2021. Dual graph U-nets for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 8160-8170 [DOI: 10.1109/JSTARS.2021.3103744http://dx.doi.org/10.1109/JSTARS.2021.3103744]

Guyon I, Weston J, Barnhill S and Vapnik V. 2002. Gene selection for cancer classification using support vector machines. Machine Learning, 46(1/3): 389-422 [DOI: 10.1023/A:1012487302797http://dx.doi.org/10.1023/A:1012487302797]

Hamilton W L, Ying R and Leskovec J. 2017. Inductive representation learning on large graphs//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc.: 1025-1035

Hong D F, Han Z, Yao J, Gao L R, Zhang B, Plaza A and Chanussot J. 2022. SpectralFormer: rethinking hyperspectral image classification with transformers. IEEE Transactions on Geoscience and Remote Sensing, 60: 5518615 [DOI: 10.1109/TGRS.2021.3130716http://dx.doi.org/10.1109/TGRS.2021.3130716]

Hu H J, Yao M L, He F and Zhang F G. 2022. Graph neural network via edge convolution for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 19: 5508905 [DOI: 10.1109/LGRS.2021.3108883http://dx.doi.org/10.1109/LGRS.2021.3108883]

Huang B X, Ge L Y, Chen G, Radenkovic M, Wang X P, Duan J M and Pan Z K. 2021. Nonlocal graph theory based transductive learning for hyperspectral image classification. Pattern Recognition, 116: 107967 [DOI: 10.1016/j.patcog.2021.107967http://dx.doi.org/10.1016/j.patcog.2021.107967]

Huang S L, Liu Z, Jin W and Mu Y. 2022. A superpixel-correlation- based multiview approach for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 19: 5504605 [DOI: 10.1109/LGRS.2021.3066000http://dx.doi.org/10.1109/LGRS.2021.3066000]

Huang Y F, Dong C H and Fan T X. 2006. Using the information of spacecraft SHEN ZHOU-3 moderate resolution imaging spectrometer to retrieve atmospheric water vapour. Journal of Remote Sensing, 10(5): 742-748

黄意玢, 董超华, 范天锡. 2006. 用神舟三号中分辨率成像光谱仪数据反演大气水汽. 遥感学报, 10(5): 742-748 [DOI: 10.11834/jrs.200605110http://dx.doi.org/10.11834/jrs.200605110]

Jan H, Zbyněk M, Lucie H, Kaplan V, Lukeš P and Cudlín P. 2008. Potentials of the VNIR airborne hyperspectral system AISA eagle//15th GIS Ostrava. Ostrava: [s.n.]

Jia S, Jiang S G, Lin Z J, Xu M, Sun W W, Huang Q, Zhu J S and Jia X P. 2022a. A semisupervised siamese network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5516417 [DOI: 10.1109/TGRS.2021.3116138http://dx.doi.org/10.1109/TGRS.2021.3116138]

Jia S, Jiang S G, Zhang S Y, Xu M and Jia X P. 2022b. Graph-in-graph convolutional network for hyperspectral image classification. IEEE Transactions on Neural Networks and Learning Systems. 20.1-15.[DOI: 10.1109/TNNLS.2022.31 82715http://dx.doi.org/10.1109/TNNLS.2022.3182715]

Joachims T. 1999. Transductive inference for text classification using support vector machines//Proceedings of the 16th International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc.: 200-209

Khan A, Vibhute A D, Mali S and Patil C H. 2022. A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecological Informatics, 69: 101678 [DOI: 10.1016/j.ecoinf.2022.101678http://dx.doi.org/10.1016/j.ecoinf.2022.101678]

Kipf T N and Welling M. 2017. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 [DOI: 10.48550/arXiv.1609.02907http://dx.doi.org/10.48550/arXiv.1609.02907]

Kunkel B, Blechinger F, Viehmann D, Piepen H V D and Doerffer R. 1991. ROSIS imaging spectrometer and its potential for ocean parameter measurements (airborne and space-borne). International Journal of Remote Sensing, 12(4): 753-761 [DOI: 10.1080/01431169108929691http://dx.doi.org/10.1080/01431169108929691]

Laban N, Abdellatif B, Ebeid H M, Shedeed H A and Tolba M F. 2018. Seasonal multi-temporal pixel based crop types and land cover classification for satellite images using convolutional neural networks//2018 13th International Conference on Computer Engineering and Systems (ICCES). Cairo: IEEE: 21-26 [DOI: 10.1109/ICCES.2018.8639232http://dx.doi.org/10.1109/ICCES.2018.8639232]

Laliberte A S, Goforth M A, Steele C M and Rango A. 2011. Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments. Remote Sensing, 3(11): 2529-2551 [DOI: 10.3390/rs3112529http://dx.doi.org/10.3390/rs3112529]

Le C F, Li Y M, Zha Y, Sun D Y, Huang C C and Lu H. 2009. A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: the case of Taihu Lake, China. Remote Sensing of Environment, 113(6): 1175-1182 [DOI: 10.1016/j.rse.2009.02.005http://dx.doi.org/10.1016/j.rse.2009.02.005]

Li F, Clausi D A, Xu L L and Wong A. 2018a. ST-IRGS: a region-based self-training algorithm applied to hyperspectral image classification and segmentation. IEEE Transactions on Geoscience and Remote Sensing, 56(1): 3-16 [DOI: 10.1109/TGRS.2017.2713123http://dx.doi.org/10.1109/TGRS.2017.2713123]

Li H Y, Zhu F J, Zheng X Y, Liu M X and Chen G Z. 2022a. MSCDUNet: a deep learning framework for built-up area change detection integrating multispectral, SAR, and VHR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 5163-5176 [DOI: 10.1109/JSTARS.2022.3181155http://dx.doi.org/10.1109/JSTARS.2022.3181155]

Li M and Zhou Z H. 2007. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 37(6): 1088-1098 [DOI: 10.1109/TSMCA.2007.904745http://dx.doi.org/10.1109/TSMCA.2007.904745]

Li Q M, Han Z C and Wu X M. 2018b. Deeper insights into graph convolutional networks for semi-supervised learning. arXiv:1801.07606 [DOI: 10.48550/arXiv.1801.07606http://dx.doi.org/10.48550/arXiv.1801.07606]

Li S T, Song W W, Fang L Y, Chen Y S, Ghamisi P and Benediktsson J A. 2019. Deep learning for hyperspectral image classification: an overview. IEEE Transactions on Geoscience and Remote Sensing, 57(9): 6690-6709 [DOI: 10.1109/TGRS.2019.2907932http://dx.doi.org/10.1109/TGRS.2019.2907932]

Li W, Wang J J, Gao Y H, Zhang M M, Tao R and Zhang B. 2022b. Graph-feature-enhanced selective assignment network for hyperspectral and multispectral data classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5526914 [DOI: 10.1109/TGRS.2022.3166252http://dx.doi.org/10.1109/TGRS.2022.3166252]

Li X Y, Fan H S, Pan S L, Jiang X H and Wu J Q. 2019. Overview of Zhuhai No. 1 hyperspectral satellite data and applications. Satellite Application, (8): 12-18

李先怡, 范海生, 潘申林, 蒋晓华, 吴佳奇. 2019. 珠海一号高光谱卫星数据及应用概况. 卫星应用, (8): 12-18 [DOI: 10.3969/j.issn.1674-9030.2019.08.005http://dx.doi.org/10.3969/j.issn.1674-9030.2019.08.005]

Li Y, Lu T and Li S T. 2020a. Subpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 4976-4988 [DOI: 10.1109/TGRS.2020.2971081http://dx.doi.org/10.1109/TGRS.2020.2971081]

Li Z T, Chen G K and Zhang T X. 2020b. A CNN-transformer hybrid approach for crop classification using multitemporal multisensor images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 847-858 [DOI: 10.1109/JSTARS.2020.2971763http://dx.doi.org/10.1109/JSTARS.2020.2971763]

Liang H B, Bao W X, Lei B B, Zhang J and Qu K W. 2020. Adaptive neighborhood strategy based generative adversarial network for hyperspectral image classification//IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa: IEEE: 862-865 [DOI: 10.1109/IGARSS39084.2020.9324088http://dx.doi.org/10.1109/IGARSS39084.2020.9324088]

Liang H B, Bao W X, Shen X F and Zhang X W. 2021. Spectral-spatial attention feature extraction for hyperspectral image classification based on generative adversarial network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 10017-10032 [DOI: 10.1109/JSTARS.2021.3115971http://dx.doi.org/10.1109/JSTARS.2021.3115971]

Liang J Y, Gao J W and Chang Y. 2009. The research and advances on semi-supervised learning. Journal of Shanxi University (Natural Science Edition), 32(4): 528-534

梁吉业, 高嘉伟, 常瑜. 2009. 半监督学习研究进展. 山西大学学报(自然科学版), 32(4): 528-534 [DOI: 10.13451/j.cnki.shanxi.univ(nat.sci.).2009.04.030http://dx.doi.org/10.13451/j.cnki.shanxi.univ(nat.sci.).2009.04.030]

Liu C, Xing C Z, Hu Q H, Wang S S, Zhao S H and Gao M. 2022. Stereoscopic hyperspectral remote sensing of the atmospheric environment: innovation and prospects. Earth-Science Reviews, 226: 103958 [DOI: 10.1016/j.earscirev.2022.103958http://dx.doi.org/10.1016/j.earscirev.2022.103958]

Liu Q C, Xiao L, Yang J X and Wei Z H. 2021. CNN-enhanced graph convolutional network with pixel- and superpixel-level feature fusion for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 59(10): 8657-8671 [DOI: 10.1109/TGRS.2020.3037361http://dx.doi.org/10.1109/TGRS.2020.3037361]

Liu Q C, Xiao L, Yang J X and Wei Z H. 2022b. Multilevel superpixel structured graph U-nets for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5516115 [DOI: 10.1109/TGRS.2021.3112586http://dx.doi.org/10.1109/TGRS.2021.3112586]

Liu Q W, Dong Y N, Zhang Y X and Luo H. 2022a. A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5530215 [DOI: 10.1109/TGRS.2022.3179419http://dx.doi.org/10.1109/TGRS.2022.3179419]

Liu W, Han L, Liu M and Li L Z. 2023. Inversion of Cd content in soil around mining area based on GaoFen-5 hyperspectral band selection. Laser and Optoelectronics Progress, 60(17): 1728001

刘雯, 韩玲, 刘明, 李良志. 2023. 基于高分五号高光谱波段选择的矿区周边土壤Cd含量反演. 激光与光电子学进展, 60(17): 1728001

Liu Y N. 2021. Development of hyperspectral imaging remote sensing technology. National Remote Sensing Bulletin, 25(1): 439-459

刘银年. 2021. 高光谱成像遥感载荷技术的现状与发展. 遥感学报, 25(1): 439-459 [DOI: 10.11834/jrs.20210283http://dx.doi.org/10.11834/jrs.20210283]

Liu Y N, Xue Y Q, Wang J Y and Shen M M. 2002. Operational modular imaging spectrometer. Journal of Infrared and Millimeter Waves, 21(1): 9-13

刘银年, 薛永祺, 王建宇, 沈鸣明. 2002. 实用型模块化成像光谱仪. 红外与毫米波学报, 21(1): 9-13 [DOI: 10.3321/j.issn:1001-9014.2002.01.003http://dx.doi.org/10.3321/j.issn:1001-9014.2002.01.003]

Long J, Liang W, Li K C, Wei Y H and Marino M D. 2023. A regularized cross-layer ladder network for intrusion detection in industrial internet of things. IEEE Transactions on Industrial Informatics, 19(2): 1747-1755 [DOI: 10.1109/TII.2022.3204034http://dx.doi.org/10.1109/TII.2022.3204034]

Lopes I, Silva A, Coimbra M, Dinis-Ribeiro M, Libanio D and Renna F. 2022. Supervised and semi-supervised training of deep convolutional neural networks for gastric landmark detection//2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Glasgow: IEEE: 2025-2028 [DOI: 10.1109/EMBC48229.2022.9870992http://dx.doi.org/10.1109/EMBC48229.2022.9870992]

Lu X C, Zhang J P, Li T and Zhang Y. 2016. A novel synergetic classification approach for hyperspectral and panchromatic images based on self-learning. IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4917-4928 [DOI: 10.1109/TGRS.2016.2553047http://dx.doi.org/10.1109/TGRS.2016.2553047]

Lucas R, Bunting P, Paterson M and Chisholm L. 2008. Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment, 112(5): 2088-2103 [DOI: 10.1016/j.rse.2007.10.011http://dx.doi.org/10.1016/j.rse.2007.10.011]

Lv Q Z, Feng W, Quan Y H, Dauphin G, Gao L R and Xing M D. 2021. Enhanced-random-feature-subspace-based ensemble CNN for the imbalanced hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 3988-3999 [DOI: 10.1109/JSTARS.2021.3069013http://dx.doi.org/10.1109/JSTARS.2021.3069013]

Ma Z T, Jiang Z G and Zhang H P. 2022. Hyperspectral image classification using feature fusion hypergraph convolution neural network. IEEE Transactions on Geoscience and Remote Sensing, 60: 5517314 [DOI: 10.1109/TGRS.2021.3123423http://dx.doi.org/10.1109/TGRS.2021.3123423]

McFee J E and Ripley H T. 1997. Detection of buried land mines using a CASI hyperspectral imager//Proceedings Volume 3079, Detection and Remediation Technologies for Mines and Minelike Targets II. Orlando: SPIE: 738-749 [DOI: 10.1117/12.280902http://dx.doi.org/10.1117/12.280902]

Mei X G, Pan E T, Ma Y, Dai X B, Huang J, Fan F, Du Q L, Zheng H and Ma J Y. 2019. Spectral-spatial attention networks for hyperspectral image classification. Remote Sensing, 11(8): 963 [DOI: 10.3390/rs11080963http://dx.doi.org/10.3390/rs11080963]

Meng X T, Bao Y L, Liu J G, Liu H J, Zhang X L, Zhang Y, Wang P, Tang H T and Kong F C. 2020. Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. International Journal of Applied Earth Observation and Geoinformation, 89: 102111 [DOI: 10.1016/j.jag.2020.102111http://dx.doi.org/10.1016/j.jag.2020.102111]

Meng X T, Bao Y L, Ye Q, Liu H J, Zhang X L, Tang H T and Zhang X H. 2021. Soil organic matter prediction model with satellite hyperspectral image based on optimized denoising method. Remote Sensing, 13(12): 2273 [DOI: 10.3390/rs13122273http://dx.doi.org/10.3390/rs13122273]

Merz C J, Clair D C and Bond W E. 1992. SeMi-supervised adaptive resonance theory (SMART2)//International Joint Conference on Neural Networks. Baltimore: IEEE: 851-856 [DOI: 10.1109/IJCNN.1992.227046http://dx.doi.org/10.1109/IJCNN.1992.227046]

Modzelewska A, Fassnacht F E and Stereńczak K. 2020. Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 84: 101960 [DOI: 10.1016/j.jag.2019.101960http://dx.doi.org/10.1016/j.jag.2019.101960]

Mou L C, Lu X Q, Li X L and Zhu X X. 2020. Nonlocal graph convolutional networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(12): 8246-8257 [DOI: 10.1109/TGRS.2020.2973363http://dx.doi.org/10.1109/TGRS.2020.2973363]

Mu H W, Zhou L, Dang X W and Yuan B. 2019. Winter wheat yield estimation from multitemporal remote sensing images based on convolutional neural networks. 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images(MultiTemp). Shanghai: IEEE: 1-4 [DOI: 10.1109/Multi-Temp.2019.8866918http://dx.doi.org/10.1109/Multi-Temp.2019.8866918]

Nigam K and Ghani R. 2000. Analyzing the effectiveness and applicability of co-training//Proceedings of the Ninth International Conference on Information Knowledge Management. McLean: ACM: 86-93 [DOI: 10.1145/354756.354805http://dx.doi.org/10.1145/354756.354805]

Odena A. 2016. Semi-supervised learning with generative adversarial networks. arXiv:1606.01583 [DOI: 10.48550/arXiv.1606.01583http://dx.doi.org/10.48550/arXiv.1606.01583]

Oerke E C, Herzog K and Toepfer R. 2016. Hyperspectral phenotyping of the reaction of grapevine genotypes to Plasmopara viticola. Journal of Experimental Botany, 67(18): 5529-5543 [DOI: 10.1093/jxb/erw318http://dx.doi.org/10.1093/jxb/erw318]

Ou DP, Tan K, Du Q, Zhu J S, Wang X and Chen Y. 2019. A novel tri-training technique for the semi-supervised classification of hyperspectral images based on regularized local discriminant embedding feature extraction. Remote Sensing, 11(6): 654 [DOI: 10.3390/rs11060654http://dx.doi.org/10.3390/rs11060654]

Padmanabha Reddy Y C A, Viswanath P and Reddy B E. 2018. Semi-supervised learning: a brief review. International Journal of Engineering and Technology, 7(1.8): 81-85 [DOI: 10.14419/ijet.v7i1.8.9977http://dx.doi.org/10.14419/ijet.v7i1.8.9977]

Pande S and Banerjee B. 2020. Dimensionality reduction using 3D residual autoencoder for hyperspectral image classification//IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa: IEEE: 2029-2032 [DOI: 10.1109/IGARSS39084.2020.9323359http://dx.doi.org/10.1109/IGARSS39084.2020.9323359]

Pise N N and Kulkarn P. 2008. A survey of semi-supervised learning methods//2008 International Conference on Computational Intelligence and Security. Suzhou: IEEE: 30-34 [DOI: 10.1109/CIS.2008.204http://dx.doi.org/10.1109/CIS.2008.204]

Pryzant R, Ermon S and Lobell D. 2017. Monitoring Ethiopian wheat fungus with satellite imagery and deep feature learning//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu: IEEE: 1524-1532 [DOI: 10.1109/CVPRW.2017.196http://dx.doi.org/10.1109/CVPRW.2017.196]

Qi Z Q, Tian Y J and Shi Y. 2012. Laplacian twin support vector machine for semi-supervised classification. Neural Networks, 35: 46-53 [DOI: 10.1016/j.neunet.2012.07.011http://dx.doi.org/10.1016/j.neunet.2012.07.011]

Qin A Y, Shang Z W, Tian J Y, Wang Y L, Zhang T P and Tang Y Y . 2019. Spectral–spatial graph convolutional networks for semisupervised hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 16(2): 241-245 [DOI: 10.1109/LGRS.2018.2869563http://dx.doi.org/10.1109/LGRS.2018.2869563]

Radford A, Metz L and Chintala S. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 [DOI: 10.48550/arXiv.1511.06434http://dx.doi.org/10.48550/arXiv.1511.06434]

Rajesh H M. 2004. Application of remote sensing and GIS in mineral resource mapping - An overview. Journal of Mineralogical and Petrological Sciences, 99(3): 83-103 [DOI: 10.2465/jmps.99.83http://dx.doi.org/10.2465/jmps.99.83]

Rasti B, Hong D F, Hang R L, Ghamisi P, Kang X D, Chanussot J and Benediktsson J A. 2020. Feature extraction for hyperspectral imagery: the evolution from shallow to deep: overview and toolbox. IEEE Geoscience and Remote Sensing Magazine, 8(4): 60-88 [DOI: 10.1109/MGRS.2020.2979764http://dx.doi.org/10.1109/MGRS.2020.2979764]

Sabale S P and Jadhav C R. 2015. Hyperspectral image classification methods in remote sensing - A review//2015 International Conference on Computing Communication Control and Automation. Pune: IEEE: 679-683 [DOI: 10.1109/ICCUBEA.2015.139http://dx.doi.org/10.1109/ICCUBEA.2015.139]

Sandino J, Pegg G, Gonzalez F and Smith G. 2018. Aerial mapping of forests affected by pathogens using UAVs, hyperspectral sensors, and artificial intelligence. Sensors, 18(4): 944 [DOI: 10.3390/s18040944http://dx.doi.org/10.3390/s18040944]

Scarselli F, Yong S L, Gori M, Hagenbuchner M, Tso A C and Maggini M. 2005. Graph neural networks for ranking web pages//The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05). Compiegne: IEEE: 666-672 [DOI: 10.1109/WI.2005.67http://dx.doi.org/10.1109/WI.2005.67]

Sellami A and Tabbone S. 2022. Deep neural networks-based relevant latent representation learning for hyperspectral image classification. Pattern Recognition, 121: 108224 [DOI: 10.1016/j.patcog.2021.108224http://dx.doi.org/10.1016/j.patcog.2021.108224]

Shahraki F F and Prasad S. 2019. Graph convolutional neural networks for hyperspectral data classification//2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Anaheim: IEEE: 968-972 [DOI: 10.1109/GlobalSIP.2018.8645969http://dx.doi.org/10.1109/GlobalSIP.2018.8645969]

Shi Y, Tian Y J, Kou G, Peng Y and Li J P. 2011. Robust support vector machines//Optimization Based Data Mining: Theory and Applications. London: Springer: 81-105 [DOI: 10.1007/978-0-85729-504-0_5http://dx.doi.org/10.1007/978-0-85729-504-0_5]

Singh P S, Singh V P, Pandey M K and Karthikeyan S. 2020. One-class classifier ensemble based enhanced semisupervised classification of hyperspectral remote sensing images//2020 International Conference on Emerging Smart Computing and Informatics (ESCI). Pune: IEEE: 22-27 [DOI: 10.1109/ESCI48226.2020.9167650http://dx.doi.org/10.1109/ESCI48226.2020.9167650]

Tang S F, Lu Z J, Wang W G and Li H. 2019. Brief description of space hyperspectral imager (invited). Infrared and Laser Engineering, 48(3): 0303003

唐绍凡, 鲁之君, 王伟刚, 李欢. 2019. 航天高光谱成像仪简述(特邀). 红外与激光工程, 48(3): 0303003

Tao X M, Chang R, Shen W, Wang R T and Li C X. 2020. Semi-supervised KFDA algorithm based on low density separation geometry distance. Journal of Software, 31(2): 493-510

陶新民, 常瑞, 沈微, 王若彤, 李晨曦. 2020. 基于低密度分割几何距离的半监督KFDA算法. 软件学报, 31(2): 493-510 [DOI: 10.13328/j.cnki.jos.005639http://dx.doi.org/10.13328/j.cnki.jos.005639]

Teloglu H K and Aptoula E. 2021. LSTM AutoEncoders applied to semi-supervised crop classification//2021 29th Signal Processing and Communications Applications Conference (SIU). Istanbul: IEEE: 1-4 [DOI: 10.1109/SIU53274.2021.9477933http://dx.doi.org/10.1109/SIU53274.2021.9477933]

Tong Q X, Zhang B and Zheng L F. 2006. Hyperspectral Remote Sensing: Principles, Techniques, and Applications. Beijing: Higher Education Press

童庆禧, 张兵, 郑兰芬. 2006. 高光谱遥感: 原理、技术与应用. 北京: 高等教育出版社

Tong Q X, Zhang B and Zhang L F. 2016. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20(5): 689-707

童庆禧, 张兵, 张立福. 2016. 中国高光谱遥感的前沿进展. 遥感学报, 20(5): 689-707 [DOI: 10.11834/jrs.20166264http://dx.doi.org/10.11834/jrs.20166264]

Tripathi P and Garg R D. 2023. Potential of DESIS and PRISMA hyperspectral remote sensing data in rock classification and mineral identification: a case study for Banswara in Rajasthan, India. Environmental Monitoring and Assessment, 195(5): 575 [DOI: 10.1007/s10661-023-11200-1http://dx.doi.org/10.1007/s10661-023-11200-1]

Vapnik V and Izmailov R. 2021. Reinforced SVM method and memorization mechanisms. Pattern Recognition, 119: 108018 [DOI: 10.1016/j.patcog.2021.108018http://dx.doi.org/10.1016/j.patcog.2021.108018]

Vapnik V N. 1999. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5): 988-999 [DOI: 10.1109/72.788640http://dx.doi.org/10.1109/72.788640]

Vincent P, Larochelle H, Lajoie I, Bengio Y and Manzagol P A. 2010. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research, 11: 3371-3408

Wan S, Gong C, Zhong P, Du B, Zhang L F and Yang J. 2020. Multiscale dynamic graph convolutional network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3162-3177 [DOI: 10.1109/TGRS.2019.2949180http://dx.doi.org/10.1109/TGRS.2019.2949180]

Wan S, Gong C, Zhong P, Pan S R, Li G Y and Yang J. 2021. Hyperspectral image classification with context-aware dynamic graph convolutional network. IEEE Transactions on Geoscience and Remote Sensing, 59(1): 597-612 [DOI: 10.1109/TGRS.2020.2994205http://dx.doi.org/10.1109/TGRS.2020.2994205]

Wan S, Yang J and Gong C. 2023. Advances of hyperspectral image classification based on graph neural networks. Acta Electronica Sinica, 51(6): 1687-1709

万升, 杨健, 宫辰. 2023. 基于图神经网络的高光谱图像分类研究进展. 电子学报, 51(6): 1687-1709 [DOI: 10.12263/DZXB.20221295http://dx.doi.org/10.12263/DZXB.20221295]

Wang H Y, Cheng Y H, Chen C L P and Wang X S. 2021a. Semisupervised classification of hyperspectral image based on graph convolutional broad network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 2995-3005[DOI: 10.1109/JSTARS.2021.3062642http://dx.doi.org/10.1109/JSTARS.2021.3062642]

Wang H Y, Cheng Y H, Chen C L P and Wang X S. 2022. Hyperspectral image classification based on domain adversarial broad adaptation network. IEEE Transactions on Geoscience and Remote Sensing, 60: 5517813 [DOI: 10.1109/TGRS.2021.3128162http://dx.doi.org/10.1109/TGRS.2021.3128162]

Wang J J, Gao F, Dong J Y and Du Q. 2021b. Adaptive DropBlock-enhanced generative adversarial networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 59(6): 5040-5053 [DOI: 10.48550/arXiv.2201.08938http://dx.doi.org/10.48550/arXiv.2201.08938]

Wang J Z, Kong L W, Huang Z C and Xiao J. 2021. Survey of graph neural network. Computer Engineering, 47(4): 1-12

王健宗, 孔令炜, 黄章成, 肖京. 2021. 图神经网络综述. 计算机工程, 47(4): 1-12 [DOI: 10.19678/j.issn.1000-3428.0058382http://dx.doi.org/10.19678/j.issn.1000-3428.0058382]

Wang K F, Gou C, Duan Y J, Lin Y L, Zheng X H and Wang F Y. 2017. Generative adversarial networks: introduction and outlook. IEEE/CAA Journal of Automatica Sinica, 4(4): 588-598 [DOI: 10.1109/JAS.2017.7510583http://dx.doi.org/10.1109/JAS.2017.7510583]

Wang L G, Hao S Y, Wang Q M and Wang Y. 2014. Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation. ISPRS Journal of Photogrammetry and Remote Sensing, 97: 123-137 [DOI: 10.1016/j.isprsjprs.2014.08.016http://dx.doi.org/10.1016/j.isprsjprs.2014.08.016]

Wang M W, Huang Z Q, Zhang X Y, Zhang Y L and Chen M L. 2021c. Altered mineral mapping based on ground-airborne hyperspectral data and wavelet spectral angle mapper tri-training model: case studies from Dehua-Youxi-Yongtai Ore District, Central Fujian, China. International Journal of Applied Earth Observation and Geoinformation, 102: 102409 [DOI: 10.1016/j.jag.2021.02409http://dx.doi.org/10.1016/j.jag.2021.02409]

Wang S, Ben H X, Hao Y B, He X N and Wang M. 2023. Boosting hyperspectral image classification with dual hierarchical learning. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(1): 21 [DOI: 10.1145/3522713http://dx.doi.org/10.1145/3522713]

Wang S, Wu L H, Jiao L C and Liu H Y. 2014. Improve the performance of co-training by committee with refinement of class probability estimations. Neurocomputing, 136: 30-40 [DOI: 10.1016/j.neucom.2014.01.039http://dx.doi.org/10.1016/j.neucom.2014.01.039]

Wu H and Prasad S. 2018. Semi-supervised deep learning using pseudo labels for hyperspectral image classification. IEEE Transactions on Image Processing, 27(3): 1259-1270 [DOI: 10.1109/TIP.2017.2772836http://dx.doi.org/10.1109/TIP.2017.2772836]

Wu Y, Mu G F, Qin C, Miao Q G, Ma W P and Zhang X R. 2020. Semi-supervised hyperspectral image classification via spatial-regulated self-training. Remote Sensing, 12(1): 159 [DOI: 10.3390/rs12010159http://dx.doi.org/10.3390/rs12010159]

Xi B B, Li J J, Li Y S and Du Q. 2021. Semi-supervised graph prototypical networks for hyperspectral image classification//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels: IEEE: 2851-2854 [DOI: 10.1109/IGARSS47720.2021.9553372http://dx.doi.org/10.1109/IGARSS47720.2021.9553372]

Xi B B, Li J J, Li Y S, Song R, Xiao Y C, Du Q and Chanussot J. 2023. Semisupervised cross-scale graph prototypical network for hyperspectral image classification. IEEE Transactions on Neural Networks and Learning Systems, 34(11): 9337-9351 [DOI: 10.1109/TNNLS.2022.3158280http://dx.doi.org/10.1109/TNNLS.2022.3158280]

Xia J H, Zhang J B, Wang Y, Han L X and Yan H. 2022. WC-KNNG-PC: watershed clustering based on k-nearest-neighbor graph and Pauta Criterion. Pattern Recognition, 121: 108177 [DOI: 10.1016/j.patcog.2021.108177http://dx.doi.org/10.1016/j.patcog.2021.108177]

Xu L Z. 2020. Research on Imaging Quality for Airborne Sweeping Hyperspectral Imager. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences

徐力智. 2020. 航空摆扫式成像光谱仪成像质量研究. 长春: 中国科学院长春光学精密机械与物理研究所

Xu M, Zhao Q Q and Jia S. 2022. Multiview spatial-spectral active learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5512415 [DOI: 10.1109/TGRS.2021.3095292http://dx.doi.org/10.1109/TGRS.2021.3095292]

Xu Y H, Du B, Zhang L P, Cerra D, Pato M, Carmona E, Prasad S, Yokoya N, Hansch R and Le Saux B. 2019. Advanced multi-sensor optical remote sensing for urban land use and land cover classification: outcome of the 2018 IEEE GRSS data fusion contest. EEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(6): 1709-1724 [DOI: 10.1109/JSTARS.2019.2911113http://dx.doi.org/10.1109/JSTARS.2019.2911113]

Yang B, Cao F L and Ye H L. 2022. A novel method for hyperspectral image classification: deep network with adaptive graph structure integration. IEEE Transactions on Geoscience and Remote Sensing, 60: 5523512 [DOI: 10.1109/TGRS.2022.3150349http://dx.doi.org/10.1109/TGRS.2022.3150349]

Yang L, Chen J J, Wang Z, Wang W J, Jiang J J, Dong X Y and Zhang W B. 2021a. Semi-supervised log-based anomaly detection via probabilistic label estimation//2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). Madrid: IEEE: 1448-1460 [DOI: 10.1109/ICSE43902.2021.00130http://dx.doi.org/10.1109/ICSE43902.2021.00130]

Yang P, Tong L, Qian B, Gao Z, Yu J and Xiao C B. 2021b. Hyperspectral image classification with spectral and spatial graph using inductive representation learning network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 791-800 [DOI: 10.1109/JSTARS.2020.3042959http://dx.doi.org/10.1109/JSTARS.2020.3042959]

Yu B X. 2000. Research on high-resolution imaging spectrometer (C-HRIS). Optics, Mechanics and Electronics Information, 17(4): 1-5

禹秉熙. 2000. 高分辨率成像光谱仪(C-HRIS)研究. 光机电信息, 17(4): 1-5 [DOI: 10.3969/j.issn.1007-1180.2000.04.001http://dx.doi.org/10.3969/j.issn.1007-1180.2000.04.001]

Yuan Y H and Lin Z Y. 2007. High spectrum remote sensing technology summary. China Water Transport, 7(8): 155-157

袁迎辉, 林子瑜. 2007. 高光谱遥感技术综述. 中国水运, 7(8): 155-157

Yuen P W T and Richardson M. 2010. An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition. The Imaging Science Journal, 58(5): 241-253 [DOI: 10.1179/174313110X12771950995716http://dx.doi.org/10.1179/174313110X12771950995716]

Zhan Y, Hu D, Wang Y T and Yu X C. 2018. Semisupervised hyperspectral image classification based on generative adversarial networks. IEEE Geoscience and Remote Sensing Letters, 15(2): 212-216 [DOI: 10.1109/LGRS.2017.2780890http://dx.doi.org/10.1109/LGRS.2017.2780890]

Zhang B W, Wang Y D, Hou W X, Wu H, Wang J D, Okumura M and Shinozaki T. 2022a. FlexMatch: boosting semi-supervised learning with curriculum pseudo labeling. arXiv:2110.08263 [DOI: 10.48550/arXiv.2110.08263http://dx.doi.org/10.48550/arXiv.2110.08263]

Zhang D and Zheng Y Q. 2013. Hyperspectral remote sensing and its development and application review. Optics and Optoelectronic Technology, 11(3): 67-73

张达, 郑玉权. 2013. 高光谱遥感的发展与应用. 光学与光电技术, 11(3): 67-73 [DOI: http://ir.ciomp.ac.cn/handle/181722/38415http://dx.doi.org/http://ir.ciomp.ac.cn/handle/181722/38415]

Zhang D Y, Zeng S Y and He W Q. 2022b. Selection and quantification of best water quality indicators using UAV-mounted hyperspectral data: a case focusing on a local river network in Suzhou City, China. Sustainability, 14(23): 16226 [DOI: 10.3390/su142316226http://dx.doi.org/10.3390/su142316226]

Zhang H Y, Zou J Q and Zhang L P. 2022c. EMS-GCN: an end-to-end Mixhop superpixel-based graph convolutional network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5526116 [DOI: 10.1109/TGRS.2022.3163326http://dx.doi.org/10.1109/TGRS.2022.3163326]

Zhang J L, Chang Y L and Shi W. 2013. Overview on label propagation algorithm and applications. Application Research of Computers, 30(1): 21-25

张俊丽, 常艳丽, 师文. 2013. 标签传播算法理论及其应用研究综述. 计算机应用研究, 30(1): 21-25 [DOI: 10.3969/j.issn.1001-3695.2013.01.004http://dx.doi.org/10.3969/j.issn.1001-3695.2013.01.004]

Zhang L P, Du B and Zhang L F. 2014. Hyperspectral Remote Sensing Image Processing. Beijing: Science Press

张良培, 杜博, 张乐飞. 2014. 高光谱遥感影像处理. 北京: 科学出版社

Zhang L P and Zhang L F. 2005. Hyperspectral Remote Sensing. Wuhan: Wuhan University Press

张良培, 张立福. 2005. 高光谱遥感. 武汉: 武汉大学出版社

Zhang T T, Li L and Zheng B J. 2013. Estimation of agricultural soil properties with imaging and laboratory spectroscopy. Journal of Applied Remote Sensing, 7(1): 073587 [DOI: 10.1117/1.JRS.7.073587http://dx.doi.org/10.1117/1.JRS.7.073587]

Zhang Y X, Liu K, Dong Y N, Wu K and Hu X Y. 2020a. Semisupervised classification based on SLIC segmentation for hyperspectral image. IEEE Geoscience and Remote Sensing Letters, 17(8): 1440-1444 [DOI: 10.1109/LGRS.2019.2945546http://dx.doi.org/10.1109/LGRS.2019.2945546]

Zhang Z, Pasolli E and Crawford M M. 2020b. An adaptive multiview active learning approach for spectral-spatial classification of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 58(4): 2557-2570 [DOI: 10.1109/TGRS.2019.2952319http://dx.doi.org/10.1109/TGRS.2019.2952319]

Zhao H Y, Xie J Z and Wang H B. 2022a. Graph convolutional network based on multi-head pooling for short text classification. IEEE Access, 10: 11947-11956 [DOI: 10.1109/ACCESS.2022.3146303http://dx.doi.org/10.1109/ACCESS.2022.3146303]

Zhao J L, Hu L, Dong Y Y, Huang L S, Weng S Z and Zhang D Y. 2021. A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification. International Journal of Applied Earth Observation and Geoinformation, 102: 102459 [DOI: 10.1016/j.jag.2021.102459http://dx.doi.org/10.1016/j.jag.2021.102459]

Zhao X F, Niu J H, Liu C T, Ding Y and Hong D F. 2022b. Hyperspectral image classification based on graph transformer network and graph attention mechanism. IEEE Geoscience and Remote Sensing Letters, 19: 6010605 [DOI: 10.1109/LGRS.2022.3182156http://dx.doi.org/10.1109/LGRS.2022.3182156]

Zhong J P, Xie W Y, Li Y S, Lei J and Du Q. 2021. Characterization of background-anomaly separability with generative adversarial network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 59(7): 6017-6028 [DOI: 10.1109/TGRS.2020.3013022http://dx.doi.org/10.1109/TGRS.2020.3013022]

Zhong S W, Zhou T, Wan S, Yang J and Gong C. 2022. Dynamic spectral-spatial Poisson learning for hyperspectral image classification with extremely scarce labels. IEEE Transactions on Geoscience and Remote Sensing, 60: 5517615 [DOI: 10.1109/TGRS.2021.3125353http://dx.doi.org/10.1109/TGRS.2021.3125353]

Zhou J L, Jing B Y, Wang Z Y, Xin H Y and Tong H H. 2022b. SODA: detecting COVID-19 in chest X-rays with semi-supervised open set domain adaptation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(5): 2605-2612 [DOI: 10.1109/TCBB.2021.3066331http://dx.doi.org/10.1109/TCBB.2021.3066331]

Zhou L H, Warner J, Nalli N R, Wei Z G, Oh Y, Bruhwiler L, Liu X P, Divakarla M, Pryor K, Kalluri S and Goldberg M D. 2023. Spatiotemporal variability of global atmospheric methane observed from two decades of satellite hyperspectral infrared sounders. Remote Sensing, 15(12): 2992 [DOI: 10.3390/rs15122992http://dx.doi.org/10.3390/rs15122992]

Zhou Y, Xie H T, Fang S C and Zhang Y D. 2022a. Semi-supervised text detection with accurate pseudo-labels. IEEE Signal Processing Letters, 29: 1272-1276 [DOI: 10.1109/LSP.2022.3175667http://dx.doi.org/10.1109/LSP.2022.3175667]

Zhou Z H. 2018. A brief introduction to weakly supervised learning. National Science Review, 5(1): 44-53 [DOI: 10.1093/nsr/nwx106http://dx.doi.org/10.1093/nsr/nwx106]

Zhou Z H and Li M. 2005. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11): 1529-1541 [DOI: 10.1109/TKDE.2005.186http://dx.doi.org/10.1109/TKDE.2005.186]

Zhou Z H and Li M. 2007. Semisupervised regression with cotraining-style algorithms. IEEE Transactions on Knowledge and Data Engineering, 19(11): 1479-1493 [DOI: 10.1109/TKDE.2007.190644http://dx.doi.org/10.1109/TKDE.2007.190644]

Zhu X J. 2008. Semi-Supervised Learning Literature Survey. Madison: University of Wisconsin-Madison

Zhu X J and Ghahramani Z. 2002. Learning from labeled and unlabeled data with label propagation. Tech Report. 3175(2004), 237-244 [DOI:10.1007/978-3-540-28649-3_29http://dx.doi.org/10.1007/978-3-540-28649-3_29]

Zou Y, Yu Z D, Kumar B V K V and Wang J S. 2018. Domain adaptation for semantic segmentation via class-balanced self-training. arXiv:1810.07911 [DOI: 10.48550/arXiv.1810.07911http://dx.doi.org/10.48550/arXiv.1810.07911]

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