Skip to main content

Advertisement

Log in

A review of deep learning used in the hyperspectral image analysis for agriculture

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can capture up to several hundred images of different wavelengths and offer relevant spectral signatures. Hyperspectral imaging technology has achieved breakthroughs in the acquisition of agricultural information and the detection of external or internal quality attributes of the agricultural product. Deep learning techniques have boosted the performance of hyperspectral image analysis. Compared with traditional machine learning, deep learning architectures exploit both spatial and spectral information of hyperspectral image analysis. To scrutinize thoroughly the current efforts, provide insights, and identify potential research directions on deep learning for hyperspectral image analysis in agriculture, this paper presents a systematic and comprehensive review. Firstly, its applications in agriculture are summarized, include ripeness and component prediction, different classification themes, and plant disease detection. Then, the recent achievements are reviewed in hyperspectral image analysis from the aspects of the deep learning models and the feature networks. Finally, the existing challenges of hyperspectral image analysis based on deep learning are summarized and the prospects of future works are put forward.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Adriana R, Carlo G, Gustau C (2015) Unsupervised deep feature extraction for remote sensing image classification. Remote Sens 54(3):1349–1362

    Google Scholar 

  • Ahmad M, Shabbir S, Oliva D, Mazzara M, Distefano S (2020a) Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification. Optik 206:163712

    Article  Google Scholar 

  • Ahmad M, Mazzara M, Raza RA, Distefano S, Sohaib A (2020b) Multiclass non-randomized spectral–spatial active learning for hyperspectral image classification. Appl Sci 10(14):4739

    Article  Google Scholar 

  • Athanasios V, Nikolaos D, Anastasios D, Eftychios P (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:1–13

    Google Scholar 

  • Awad MM (2019) An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation. Inf Process Agric 6(3):316–325

    MathSciNet  Google Scholar 

  • Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder–decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  • Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153

    Google Scholar 

  • Bengio Y, Courville A, Vincent P (2012) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  • Bhardwaj K, Das A, Patra S (2020a) Spectral–spatial active learning with superpixel profile for classification of hyperspectral images. In: 2020 6th international conference on signal processing and communication (ICSC), pp 149–155. https://doi.org/10.1109/ICSC48311.2020.9182764

  • Bhardwaj K, Das A, Patra S (2020b) Spectral–spatial active learning with attribute profile for hyperspectral image classification. In: International conference on intelligent computing and smart communication 2019. Springer, Singapore, pp 1219–1229. https://doi.org/10.1007/978-981-15-0633-8_119

  • Bharti R, Saini D, Malik R (2021) A novel approach for hyper spectral images using transfer learning. IOP Conf Ser Mater Sci Eng 1022(1):012120

    Article  Google Scholar 

  • Bioucas-Dias JM, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N, Chanussot J (2013) Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci Remote Sens Mag 1(2):6–36

    Article  Google Scholar 

  • Cao XY, Yao J, Xu ZB, Meng DY (2020) Hyperspectral image classification with convolutional neural network and active learning. IEEE Trans Geosci Remote Sens 58(7):4604–4616. https://doi.org/10.1109/TGRS.2020.2964627

    Article  Google Scholar 

  • Caballero D, Calvini R, Amigo JL (2020) Hyperspectral imaging in crop fields: precision agriculture. Data Handl Sci Technol 32:453–473

    Article  Google Scholar 

  • Chen YS, Lin ZH, Zhao X, Wang G, Gu YF (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2094–2107

    Article  Google Scholar 

  • Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2015a) Semantic image segmentation with deep convolutional nets and fully connected CRFs. Paper presented at the international conference on learning representations 40(4):834–848

    Google Scholar 

  • Chen YS, Zhao X, Jia XP (2015b) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2381–2392

    Article  Google Scholar 

  • Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016a) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251

    Article  Google Scholar 

  • Chen X, Ma L, Yang XQ (2016b) Stacked denoise autoencoder based feature extraction and classification for hyperspectral images. Journal of Sensors 3632943. https://doi.org/10.1155/2016/3632943

  • Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017a) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  • Chen ZY, Li WH (2017) Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans Instrum Meas 99:1–10

    Google Scholar 

  • Chen L, Papandreou G, Schroff F, Adam H (2017b) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587

  • Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV). Springer, Cham, pp 833–851. https://doi.org/10.1007/978-3-030-01234-2_49

  • Chen FJ, Li JM, Yang DY (2019) Hyperspectral image classification based on generative adversarial networks. Comput Eng Appl 55(22):172–179

    Google Scholar 

  • Chung J, Gulcehre C, Cho K H et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

  • Cui Y, Ji XW, Xu K, Wang LG (2019) A double-strategy-check active learning algorithm for hyperspectral image classification. Photogramm Eng Remote Sens 85(11):841–851

    Article  Google Scholar 

  • Dan C, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the 22nd international joint conference on artificial intelligence, pp 1237–1242. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-210

  • Fan YY, Zhang C, Liu ZY, Qiu ZJ, He Y (2019) Cost-sensitive stacked sparse auto-encoder models to detect striped stem borer infestation on rice based on hyperspectral imaging. Knowl-Based Syst 168(2019):49–58

    Article  Google Scholar 

  • Fauvel M, Tarabalka Y, Benediktsson JA, Chanussot J, Tilton JC (2013) Advances in spectral–spatial classification of hyperspectral images. Proc IEEE 101(3):652–675

    Article  Google Scholar 

  • Feng Z, Wang M, Yang S (2017) Superpixel tensor sparse coding for structural hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 4:1–8

    Google Scholar 

  • Fricker GA, Ventura JD, Wolf JA, North MP (2019) A convolutional neural network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery. Remote Sens 11(19):2326–2347

    Article  Google Scholar 

  • Gan HM, Yue XJ, Hong TS, Ling KJ, Wang LH, Cen ZZ (2018) A hyperspectral inversion model for predicting chlorophyll content of Longan leaves based on deep learning. J South China Agric Univ 39(3):102–110

    Google Scholar 

  • Gao H, Yao D, Wang M et al (2019) A hyperspectral image classification method based on multi-discriminator generative adversarial networks. Sensors 19(15):3269

    Article  Google Scholar 

  • Garcia-Garcia A, Orts-Escolano S, Oprea SO, Villena-Martinez V, Garcia-Rodriguez J (2017) A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857

  • Guo TF, Huang M, Zhu QB, Guo Y (2017) Hyperspectral image-based spare autoencoder network for TVB-N measurement in pork.  In: 2017 ASABE annual international meeting, 1700450. https://doi.org/10.13031/aim.201700450

  • Guo YM, Liu Y, Ard O, Lao SY (2016) Deep learning for visual understanding: a review. Neurocomputing 187(Apr.26):27–48

    Article  Google Scholar 

  • Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of the international conference on neural information processing systems, Montreal, QC, Canada, pp 2672–2680

  • Han LQ, Zhang YN, Qin QM (2019) Endmember extraction of farmland hyperspectral image using deep learning autoencoder and shuffled frog leaping algorithm. Trans Chin Soc Agric Eng 35(6):167–173

    Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR),  pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  • He Z, Hu J, Wang YW (2018) Low-rank tensor learning for classification of hyperspectral image with limited labeled samples. Signal Process 145(2018):12–25

    Article  Google Scholar 

  • He ZP, Xia KW, Li TJ, Zu BK, Yin ZX, Zhang JN (2021) A constrained graph-based semi-supervised algorithm combined with particle cooperation and competition for hyperspectral image classification. Remote Sens 13(2):193

    Article  Google Scholar 

  • He Z, Liu H, Wang YW, Hu J (2017) Generative adversarial networks-based semi-supervised learning for hyperspectral image classification. Remote Sens 9(10):1042

    Article  Google Scholar 

  • Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  • Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  • Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  • Hu W, Huang YY, Wei L, Zhang F, Li HC (2015) Deep convolutional neural networks for hyperspectral image classification. J Sens 2015:1–12

    Article  Google Scholar 

  • Hu RW, Yu Y, Ni ML, Yu J, Zhao JW, Zhu C, Li ZM (2020) Identification of lotus seed flour adulteration based on near-infrared spectroscopy combined with deep belief network. Food Sci 41(06):298–303

    Google Scholar 

  • Huang SP, Sun C, Qi L, Ma X, Wang WJ (2017) Rice panicle blast identification method based on deep convolution neural network. Trans Chin Soc Agric Eng 33(20):169–176

    Google Scholar 

  • Huang Y, Tang LB, Li Z, Long T (2019b) Research on peanut planting area classification technology using remote sensing image based deep learning. J Signal Process 35(4):617–622

    Google Scholar 

  • Huang FH, Yu Y, Feng TH (2019) Hyperspectral remote sensing image change detection based on tensor and deep learning. J Vis Commun Image Represent 58(JAN.):233–244

    Article  Google Scholar 

  • Ishida T, Kurihara J, Viray FA, Namuco SB, Marciano JJ (2018) A novel approach for vegetation classification using UAV-based hyperspectral imaging. Comput Electron Agric 144:80–85

    Article  Google Scholar 

  • Jaime Z, Ren JC, Zheng JB, Zhao HM, Qing CM, Yang ZJ, Stephen M (2016) Novel segmented stacked auto-encoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185(12):1–10

    Google Scholar 

  • Jamshidpour N, Safari A, Homayouni S (2020) A GA-based multi-view, multi-learner active learning framework for hyperspectral image classification. Remote Sens 12(2):297

    Article  Google Scholar 

  • Ji SP, Zhang C, Xu AJ, Shi Y, Duan YL (2018) 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens 10(2):75–92

    Article  Google Scholar 

  • Jiao LC, Liang MM, Chen H, Yang SY, Liu HY, Cao XH (2017) Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(10):5585–5599

    Article  Google Scholar 

  • Jiang ZC, Pan WD, Shen H (2018) LSTM based adaptive filtering for reduced prediction errors of hyperspectral images. In: 2018 6th IEEE international conference on wireless for space and extreme environments (WISEE), pp 158–162. https://doi.org/10.1109/WiSEE.2018.8637354

  • Jiang XF, Zhang Y, Li Y, Li SY, Zhang Y (2019) Hyperspectral image classification with transfer learning and Markov random fields. IEEE Geosci Remote Sens Lett 17(3):544–548. https://doi.org/10.1109/LGRS.2019.2923647

    Article  Google Scholar 

  • Jiang YP, Chen SF, Bian B, Li YH, Sun Y, Wang XC (2021) Discrimination of tomato maturity using hyperspectral imaging combined with graph-based semi-supervised method considering class probability information. Food Anal Methods 2:1–16

    Google Scholar 

  • Jin G, Raich R (2012) On surrogate supervision multiview learning. In: IEEE international workshop on machine learning for signal processing (MLSP), pp 1–6. https://doi.org/10.1109/MLSP.2012.6349759

  • Jin X, Jie L, Wang S, Qi H, Li S (2018) Classifying wheat hyperspectral pixels of healthy heads and fusarium head blight disease using a deep neural network in the wild field. Remote Sens 10(3):395–415

    Article  Google Scholar 

  • Jin X, Lu J, Fu YZ, Wang S, Xu GJ, Li SW (2019) A classification method for hyperspectral imaging of Fusarium head blight disease symptom based on deep convolutional neural network. Acta Agriculturae Zhejiangensis 31(2):315–325

    Google Scholar 

  • Kemker R, Kanan C (2017) Self-taught feature learning for hyperspectral image classification. Remote Sens 55(5):2693–2705

    Article  Google Scholar 

  • Kong Y, Wang XS, Cheng YH (2018) Spectral–spatial feature extraction for HSI classification based on supervised hypergraph and sample expanded. IEEE J Sel Top Appl Earth Obs Remote Sens 11(11):4128–4140

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems,  pp 1097–1105

  • Kumar S, Torres C, Ulutan O et al (2020) Deep remote sensing methods for methane detection in overhead hyperspectral imagery. In: Winter conference on applications of computer vision (WACV), pp 1765–1774. https://doi.org/10.1109/WACV45572.2020.9093600

  • Kuska M, Wahabzada M, Leucker M, Dehne HW, Kersting K, Oerke EC, Steiner U, Mahlein AK (2015) Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant pathogen interactions. Plant Methods 11(1):28–41

    Article  Google Scholar 

  • Lecun Y, Bottou L (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  • Lee H, Heesung K (2016) Contextual deep CNN based hyperspectral classification. In: 2016 IEEE international geoscience and remote sensing symposium (IGARSS)

  • Lee H, Kwon H (2017) Going deeper with contextual CNN for hyperspectral image classification. IEEE Trans Image Process 26(10):4843–4855

    Article  MathSciNet  Google Scholar 

  • Lei Z, Zeng Y, Liu P, Su XH (2021) Active deep learning for hyperspectral image classification with uncertainty learning. IEEE Geosci Remote Sens Lett 99:1–5

    Google Scholar 

  • Li Y, Zhang HK, Shen Q (2017a) Spectral–spatial classification of hyperspectral imagery with 3-D convolutional neural network. Remote Sens 1(9):67–87

    Article  Google Scholar 

  • Li W, Wu GD, Zhang F, Du Q (2017b) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 52(2):844–853

    Article  Google Scholar 

  • Li JJ, Zhao X, Li YS, Du Q, Xi BB, Hu J (2018a) Classification of hyperspectral imagery using a new fully convolutional neural network. IEEE Geosci Remote Sens Lett 15(2):292–296

    Article  Google Scholar 

  • Li JJ, Xi BB, Li YS, Du Q, Wang KY (2018b) Hyperspectral classification based on texture feature enhancement and deep belief networks. Remote Sens 10(3):396–416

    Article  Google Scholar 

  • Li S, Song W, Fang L, Chen Y, Benediktsson JA (2019) Deep learning for hyperspectral image classification: an overview. IEEE Trans Geosci Remote Sens 57(9):6690–6709

    Article  Google Scholar 

  • Li Y, Lu T, Li ST (2020) Subpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification. IEEE Trans Geosci Remote Sens 99:1–13

    Google Scholar 

  • Li XG, Huang XQ (2016a) Deep neural networks based on hyperspectral image classification. Electron Meas Technol 39(7):81–86

    MathSciNet  Google Scholar 

  • Li XY, Ku J, Yan YY, Xu ML, Xu SM, Jin R (2016b) Detection method of green potato based on hyperspectral imaging. Trans Chin Soc Agric Mach 47(3):228–233

    Google Scholar 

  • Li J, Jose M (2013) Semi-supervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Lett Geosci Remote Sens 10:318–322

    Article  Google Scholar 

  • Lin ZH, Chen Y, Zhao X, Wang G (2014) Spectral–spatial classification of hyperspectral image using autoencoders. In: 2013 9th international conference on information, communications and signal processing, pp 1–5. https://doi.org/10.1109/ICICS.2013.6782778

  • Liu B, Yu XC, Zhang PQ, Tan X, Yu AZ, Xue ZX (2017b) A semi-supervised convolutional neural network for hyperspectral image classification. Remote Sens Lett 8(9):839–848

    Article  Google Scholar 

  • Liu XF, Sun QQ, Liu B, Huang B (2017a) Hyperspectral image classification based on convolutional neural network and dimension reduction. In: 2017 Chinese Automation Congress (CAC),  pp 1686–1690. https://doi.org/10.1109/CAC.2017.8243039

  • Liu QS, Zhou F, Hang RL, Yuan XT (2017c) Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sens 9(12):1330

    Article  Google Scholar 

  • Liu P, Zhang H, Eom KB (2017d) Active deep learning for classification of hyperspectral images. IEEE J Sel Top Appl Earth Obs Remote Sens 10(2):712–724

    Article  Google Scholar 

  • Liu XF, Sun QQ, Meng Y, Wang CC, Fu M (2018a) Feature extraction and classification of hyperspectral image based on 3D-convolution neural network. In: 2018 IEEE 7th data driven control and learning systems conference, pp 918–922. https://doi.org/10.1109/DDCLS.2018.8515930

  • Liu B, Yu XC, Yu AZ, Zhang PQ, Wan G, Wang RR (2018b) Deep few-shot learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 99:1–15

    Google Scholar 

  • Liu B, Yu XC, Zhang PQ, Yu AZ, Fu QY, Wei XP (2018c) Supervised deep feature extraction for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(4):1909–1921

    Article  Google Scholar 

  • Liu CY, He L, Li ZT, Li J (2018d) Feature-driven active learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(1):341–354

    Article  Google Scholar 

  • Liu JJ, Wu ZB, Xiao L, Sun J, Yan H (2019) Generalized tensor regression for hyperspectral image classification. IEEE Trans Geosci Remote Sens 58(2):1244–1258

    Article  Google Scholar 

  • Liu CL, Lin L, Yu CC, Wu JZ (2020a) Research on peanut hyperspectral image classification method based on deep learning. Comput Simul 37(03):189–192

    Google Scholar 

  • Liu X, Qiao Y, Xiong Y, Cai Z, Liu P (2020b) Cascade conditional generative adversarial nets for spatial–spectral hyperspectral sample generation. Sci China Inf Sci 63(4):1–16

    Article  Google Scholar 

  • Liu Z, Jiang J, Qiao X et al (2020) Using convolution neural network and hyperspectral image to identify moldy peanut kernels. LWT Food Sci Technol 132:109815

    Article  Google Scholar 

  • Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651

    Google Scholar 

  • Lu W, Guo YM, Dai DJ, Zhang CY, Wang XY (2018) Rice germination rate detection based on fluorescent spectrometry and deep belief network. Spectrosc Spectr Anal 38(4):1303–1312

    Google Scholar 

  • Lu HX, Wei MM, Yang HH, Liu ZB, Hu JQ (2019) Detecting huanglongbing by stacked denoising auto-encoders combined random forest. Laser Infrared 49(4):460–466

    Google Scholar 

  • Luo JH, Li MQ, Zhang ZZ, Li J (2017) Hyperspectral remote sensing images classification using a deep convolutional neural network model. J Xihua Univ 36(4):13–20

    Google Scholar 

  • Ma XR, Geng J, Wang HY (2015) Hyperspectral image classification via contextual deep learning. Eurasip J Image Video Process 1:20

    Article  Google Scholar 

  • Makantasis K, Karantzalos K, Doulamis A, Loupos K (2015a) Deep learning-based man-made object detection from hyperspectral data. In: International symposium on visual computing, pp 717–727. https://doi.org/10.1007/978-3-319-27857-5_64

  • Makantasis K, Doulamis ND, Nikitakis A, Doulamis AD (2018a) Tensor-based classification models for hyperspectral data analysis. IEEE Trans Geosci Remote Sens 56(12):6884–6898

    Article  Google Scholar 

  • Mou L, Ghamisi P, Zhu XX (2017a) Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(1):391–406

    Article  Google Scholar 

  • Mou LC, Ghamisi P, Zhu XX (2017b) Deep recurrent neural networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(7):3639–3655

    Article  Google Scholar 

  • Mu CH, Liu J, Liu Y, Liu YJ (2020) Hyperspectral image classification based on active learning and spectral–spatial feature fusion using spatial coordinates (October 2019). IEEE Access 8:11

    Article  Google Scholar 

  • Mughees A, Tao L (2017) Hyperspectral image classification based on deep auto-encoder and hidden Markov random field. In: 13th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), pp 59–65. https://doi.org/10.1109/FSKD.2017.8393336

  • Munoz-Mari J, Bovolo F, Gmez-Chova L, Bruzzone L, Camp-Valls G (2010) Semi-supervised one-class support vector machines for classification of remote sensing data. IEEE Trans Geosci Remote Sens 48(8):3188–3197

    Article  Google Scholar 

  • Murphy JM, Maggioni M (2018) Unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion. IEEE Trans Geosci Remote Sens 57(3):1829–1845

    Article  Google Scholar 

  • Nagasubramanian K, Jones S, Singh AK, Singh A, Ganapathysubramanian B, Sarkar S (2017) Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps. In: 31st conference on neural information processing systems (NIPS, 2017) 4–9 December 2017, Long Beach, CA, USA

  • Nataliia K, Mykola L, Sergii S, Andrii S (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14(5):778–782

    Article  Google Scholar 

  • Ni C, Li ZY, Zhang X, Zhao L, Zhu TT, Jiang XS (2019) Film sorting algorithm in seed cotton based on near-infrared hyperspectral image and deep learning. Trans CSAE 50(12):170–179

    Google Scholar 

  • Nie P, Zhang J, Feng X, Yu C, He Y (2019) Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sens Actuators B Chem 296:126630–126641

    Article  Google Scholar 

  • Niu Z, Liu W, Zhao J et al (2019) DeepLab-based spatial feature extraction for hyperspectral image classification. IEEE Geosci Remote Sens Lett 16(2):251–255

    Article  Google Scholar 

  • Ozdemir AO, Gedik BE, Cetin YY (2014) Hyperspectral classification using stacked autoencoders with deep learning. In: Proceedings of the 2014 6th workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS), pp 1–4. https://doi.org/10.1109/WHISPERS.2014.8077532

  • Pan B, Shi ZW, Xu X (2017) R-VCANet: a new deep-learning-based hyperspectral image classification method. IEEE J Sel Top Appl Earth Obs Remote Sens 10(5):1975–1986

    Article  Google Scholar 

  • Paoletti ME, Haut JM, Plaza J, Plaza A (2018) A new deep convolutional neural network for fast hyperspectral image classification. ISPRS J Photogramm Remote Sens 145(2018):120–147

    Article  Google Scholar 

  • Paoletti ME, Haut JM, Plaza J, Plaza A (2020) Training Capsnets via active learning for hyperspectral image classification. In: IGARSS 2020-2020 IEEE international geoscience and remote sensing symposium, pp 40–43. https://doi.org/10.1109/IGARSS39084.2020.9324302

  • Petersson H, Gustafsson D, Bergstrom D (2016) Hyperspectral image analysis using deep learning: a review. In: International conference on image processing theory, tools and applications, pp 1–6. https://doi.org/10.1109/IPTA.2016.7820963

  • Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP (2017) Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience 6(10):1–10

    Article  Google Scholar 

  • Pradhan MK, Minz S, Shrivastava VK (2019a) A kernel-based extreme learning machine framework for classification of hyperspectral images using active learning. J Indian Soc Remote Sens 47(3):1693

    Article  Google Scholar 

  • Pradhan MK, Minz S, Shrivastava VK (2019b) Fast active learning for hyperspectral image classification using extreme learning machine. IET Image Process 13(4):549–555

    Article  Google Scholar 

  • Protopapadakis E, Doulamis A, Doulamis N, Maltezos E (2021) Stacked autoencoders driven by semi-supervised learning for building extraction from near infrared remote sensing imagery. Remote Sens 13(3):371

    Article  Google Scholar 

  • Rao LB, Pang T, Ji RS, Chen XY, Zhang J (2019) Firmness detection for apples based on hyperspectral imaging technology combined with stack autoencoder-extreme learning machine method. Laser Optoelectron Progr 56(11):113001-1-113001–7

    Google Scholar 

  • Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Paper presented at the medical image computing and computer assisted intervention 759:195–202

    Google Scholar 

  • Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang ZH, Karpathy A, Khosla A, Bernstein M, Berg AC, Li FF (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  • Sawant SS, Prabukumar M (2018) A review on graph-based semi-supervised learning methods for hyperspectral image classification. Egypt J Remote Sens Space Sci 23(2):243–248

    Google Scholar 

  • Simonyan K, Zisserman A (2015) Very deep convolutional networks for arge-scale image recognition. In: International conference on learning representations, 7–9 (May 2015) San Diego, CA, pp 1–14

  • Simranjit S, Singara SK (2018) Efficient classification of the hyperspectral images using deep learning. Multimed Tools and Applications 77:27061–27074

    Article  Google Scholar 

  • Song A, Choi J, Han Y, Kim Y (2018) Change detection in hyperspectral images using recurrent 3D fully convolutional networks. Remote Sens 10(11):1827

    Article  Google Scholar 

  • Slavkovikj V, Verstockt S, De Neve W, Van Hoecke S, Van De Walle R (2015) Hyperspectral image classification with convolutional neural networks. In: Proceedings of the 23rd annual ACM conference on multimedia conference, 13–15 October 2015, Brisbane, Australia, pp 1159–1162

  • Steinbrener J, Posch K, Leitner R (2019) Hyperspectral fruit and vegetable classification using convolutional neural networks. Comput Electron Agric 162:364–372

    Article  Google Scholar 

  • Sun Z, Wang C (2014) Semi-supervised classification for hyperspectral imagery with transductive multiple-kernel learning. IEEE Lett Geosci Remote Sens 11:1991–1995

    Article  Google Scholar 

  • Sun QQ, Liu XF, Fu M (2017) Classification of hyperspectral image based on principal component analysis and deep learning. In: 2017 7th IEEE international conference on electronics information and emergency communication (ICEIEC),  pp 356–359. https://doi.org/10.1109/ICEIEC.2017.8076581

  • Sun J, Jin HT, Wu XH, Lu H, Shen JF, Dai CX (2018a) Tea variety identification based on low-rank stacked auto-encoder and hyperspectral image. Trans CSAE 49(8):316–323

    Google Scholar 

  • Sun Y, Wei KL, Liu Q, Pan LQ, Tu K (2018b) Classification and discrimination of different fungal diseases of three infection levels on peaches using hyperspectral reflectance imaging analysis. Sensors 18(4):1295–1308

    Article  Google Scholar 

  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich, A (2014) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

  • Tao C, Pan HB, Li YS, Zou ZR (2015) Unsupervised spectral–spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification. IEEE Geosci Remote Sens Lett 12(12):2438–2442

    Article  Google Scholar 

  • Tasissa A, Nguyen D, Murphy J (2021) Deep diffusion processes for active learning of hyperspectral images. arXiv preprint arXiv:2101.03197

  • Tian YP, Tao C, Zou ZR, Yang ZX, He XF (2015) Semi-supervised graph-based hyperspectral image classification with active learning. Acta Geodaetica et Cartographica Sinica 44(8):919–926

    Google Scholar 

  • Tuia D, Camps-Valls G (2009) Semi-supervised remote sensing image classification with cluster kernels. IEEE Geosci Remote Sens Lett 6(2):224–228

    Article  Google Scholar 

  • Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning,  pp 1096–1103. https://doi.org/10.1145/1390156.1390294

  • Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12):3371–3408

    MathSciNet  MATH  Google Scholar 

  • Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:1–13

    Google Scholar 

  • Wang C, Liu Y, Bai X, Tang WZ, Lei P, Zhou J (2017) Deep residual convolutional neural network for hyperspectral image super-resolution. In: International conference on image and graphics, pp 370–380. https://doi.org/10.1007/978-3-319-71598-8_33

  • Wang DY, Vinson R, Holmes M, Seibel G, Bechar A, Nof S, Tao Y (2019) Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). Sci Rep 9(1):4377–4390

    Article  Google Scholar 

  • Wang HY, Li XF, Li YB, Sun YX, Xu HL (2020) Non-destructive detection of apple multi-quality parameters based on hyperspectral imaging technology. J Nanjing Agric Univ 43(1):178–185

    Google Scholar 

  • Williams Z (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1(2):270–280. https://doi.org/10.1162/neco.1989.1.2.270

    Article  Google Scholar 

  • Wu H, Saurabh P (2017) Semi-supervised deep learning using pseudo labels for hyperspectral image classification. IEEE Trans Image Process 27(3):1259–1270

    Article  MathSciNet  MATH  Google Scholar 

  • Xie ZZ, Xu HL, Huang QG, Wang P (2019) Spinach freshness detection based on hyperspectral image and deep learning method. Trans Chin Soc Agric Eng 35(13):277–284

    Google Scholar 

  • Xie F, Gao Q, Jin C, Zhao F (2021) Hyperspectral image classification based on superpixel pooling convolutional neural network with transfer learning. Remote Sens 13(5):930

    Article  Google Scholar 

  • Xu YH, Bo D, Zhang LP (2019) Beyond the patchwise classification: spectral–spatial fully convolutional networks for hyperspectral image classification. IEEE Trans Big Data 6(3):492–506

    Article  MathSciNet  Google Scholar 

  • Xue ZX (2020) A general generative adversarial capsule network for hyperspectral image spectral–spatial classification. Remote Sens Lett 11(1):19–28

    Article  Google Scholar 

  • Yang JX, Zhao YQ, Chan CW, Chen Y (2016) Hyperspectral image classification using two-channel deep convolutional neural network. In: 2016 IEEE international geoscience and remote sensing symposium (IGARSS), pp 5079–5082. https://doi.org/10.1109/IGARSS.2016.7730324

  • Yang GG, Bao YD, Liu ZY (2017) Localization and recognition of pests in tea plantation based on image saliency analysis and convolutional neural network. Trans Chin Soc Agric Eng 33(6):156–162

    Google Scholar 

  • Yang XF, Ye YM, Li XT, Lau R, Zhang XF, Huang XH (2018a) Hyperspectral image classification with deep learning models. IEEE Trans Geosci Remote Sens 56(9):5408–5423

    Article  Google Scholar 

  • Yang G, Gewali UB, Ientilucci E et al (2018b) Dual-channel densenet for hyperspectral image classification. In: 2018 IEEE international geoscience and remote sensing symposium, pp 2595–2598. https://doi.org/10.1109/IGARSS.2018.8517520

  • Yang JG, Guo YH, Wang XL (2019) Feature extraction of hyperspectral images based on deep Boltzmann machine. IEEE Geosci Remote Sens Lett 17(6):1077–1081. https://doi.org/10.1109/LGRS.2019.2937601

    Article  Google Scholar 

  • Yi K, Wang X, Cheng Y, Chen C (2018) Hyperspectral imagery classification based on semi-supervised broad learning system. Remote Sens 10(5):685

    Article  Google Scholar 

  • Yoo Hyeon-Joong (2015) Deep convolution neural networks in computer vision. IEIE Trans Smart Process Comput 4(1):35–43

    Article  Google Scholar 

  • Yu XJ, Lu HD, Liu QY (2018a) Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf. Chemometr Intell Lab Syst 172(2018):188–193

    Article  Google Scholar 

  • Yu XJ, Lu HD, Wu D (2018b) Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging. Postharvest Biol Technol 141(2018):39–49

    Article  Google Scholar 

  • Yu XJ, Tang L, Wu XF, Lu HD (2018c) Nondestructive freshness discriminating of shrimp using visible/near-Infrared hyperspectral Imaging technique and deep learning algorithm. Food Anal Methods 11:768–780

    Article  Google Scholar 

  • Yu XJ, Yu X, Wen ST, Yang JQ, Wang JP (2019a) Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp. J Food Meas Charact 2(2019):2082–2094

    Article  Google Scholar 

  • Yu XJ, Wang JP, Wen ST, Yang JQ, Zhang FF (2019b) A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N (total volatile basic nitrogen (TVB-N) content in Pacific white shrimp (Litopenaeus vannamei). Biosyst Eng 178(2019):244–255

    Article  Google Scholar 

  • Yuan Y, Zheng X, Lu X (2017) Hyperspectral image superresolution by transfer learning. IEEE J Sel Top Appl Earth Obs Remote Sens 5:1–12

    Google Scholar 

  • Yuan QQ, Zhang Q, Li J, Shen HF, Zhang LP (2018) Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network. IEEE Trans Geosci Remote Sens 57(2):1205–1218

    Article  Google Scholar 

  • Yue J, Zhao WZ, Mao SJ, Liu H (2015) Spectral–spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sens Lett 6(6):468–477

    Article  Google Scholar 

  • Yue J, Mao SJ, Mei L (2016) A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sens Lett 7(9):875–884

    Article  Google Scholar 

  • Yue XJ, Ling KJ, Wang LH, Cen ZZ, Lu Y, Liu YX (2019) Inversion of potassium content for citrus leaves based on hyperspectral and deep transfer learning. Trans CSAE 50(3):186–195

    Google Scholar 

  • Zeiler M, Fergus R (2014) Visualizing and understanding convolutional neural networks. ECCV 2014, pp 818–833

  • Zhan Y, Hu D, Wang Y, Yu X (2018) Semisupervised hyperspectral image classification based on generative adversarial networks. IEEE Geosci Remote Sens Lett 15(2):212–216

    Article  Google Scholar 

  • Zhang L, Zhang L, Tao D, Huang X (2013) Tensor discriminative locality alignment for hyperspectral image spectral–spatial feature extraction. IEEE Trans Geosci Remote Sens 51(1):242–256

    Article  Google Scholar 

  • Zhang X (2014) Modified co-training with spectral and spatial views for semi-supervised hyperspectral image classification. Appl Earth Obs Remote Sens IEEE 7:2044–2055

    Article  Google Scholar 

  • Zhang C, Guo C, Liu F, Kong W, He Y, Lou B (2016) Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine. J Food Eng 179(2016):11–18

    Article  Google Scholar 

  • Zhang HK, Li Y, Zhang YZ, Shen Q (2017) Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network. Remote Sens Lett 8(5):438–447

    Article  Google Scholar 

  • Zhang MM, Li W, Du Q (2018) Diverse region-based CNN for hyperspectral image classification. IEEE Trans Image Process 27(6):2623–2634

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang M, Jiang Y, Li C et al (2020a) Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging. Biosyst Eng 192:159

    Article  Google Scholar 

  • Zhang Z, Pasolli E, Crawford MM (2020b) An adaptive multiview active learning approach for spectral–spatial classification of hyperspectral images. IEEE Trans Geosci Remote Sens 58(4):2557–2570

    Article  Google Scholar 

  • Zhao WZ, Du SH (2016) Spectral–spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544–4554

    Article  Google Scholar 

  • Zhao S, Qiu Z, He Y (2021) Transfer learning strategy for plastic pollution detection in soil: calibration transfer from high-throughput HSI system to NIR sensor. Chemosphere 7:129908

    Article  Google Scholar 

  • Zhong Z, Fan B, Duan J et al (2015) Discriminant tensor spectral–spatial feature extraction for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(5):1028–1032

    Article  Google Scholar 

  • Zhong ZL, Li J, Ma LF, Jiang H, Zhao H (2017a) Deep residual networks for hyperspectral image classification. In: IEEE international geoscience and remote sensing symposium (IGARSS), pp 1824–1827. https://doi.org/10.1109/IGARSS.2017.8127330

  • Zhong P, Gong ZQ, Li ST, Schonlieb CB (2017b) Learning to diversify deep belief networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(6):3516–3530

    Article  Google Scholar 

  • Zhong P, Gong ZQ (2017) A hybrid DBN and CRF model for spectral–spatial classification of hyperspectral images. Stat Optim Inf Comput 5(2):75

    Article  MathSciNet  Google Scholar 

  • Zhou ZY, He DJ, Zhang HH, Lei Y, Su D, Chen K (2017) Non-destructive detection of moldy core in apple fruit based on deep belief network. Food Sci 38(14):297–303

    Google Scholar 

  • Zhou X, Sun J, Tian Y, Chen QS, Wu XH, Hang YY (2020) A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves. Chemometr Intell Lab Syst 200(15):103996

    Google Scholar 

  • Zhu L, Chen Y, Ghamisi P, Benediktsson JA (2018) Generative adversarial networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(9):5046–5063

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by NSFC (Nos. 31871543, 31700644), Natural Science Foundation of Shandong (No. ZR2020KF002), and the project of Shandong provincial key laboratory of horticultural machinery and equipment (No. YYJX201905). The authors are grateful to all study participants. The authors declared that they have no conflicts of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Ping Liu or Xiang Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Liu, B., Liu, L. et al. A review of deep learning used in the hyperspectral image analysis for agriculture. Artif Intell Rev 54, 5205–5253 (2021). https://doi.org/10.1007/s10462-021-10018-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-021-10018-y

Keywords

Navigation