Skip to main content
SpringerLink
Log in

Hyperspectral image segmentation: a comprehensive survey

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Hyperspectral Images, which are high-dimensional in nature and capture bands over hundreds of wavelengths of the electromagnetic spectrum. These images have piqued researchers’ curiosity in the last two decades. The purpose of this paper is to investigate how researchers segmented and classified Hyperspectral Images with unbalanced data and few labelled training examples. For the sake of comprehension, the background of Hyperspectral Images and segmentation techniques is briefly discussed at first. The study is organised around different Hyperspectral Image processing techniques such as thresholding, clustering, watershed, deep learning, and other methods. The recent trends and developments in HSI segmentation have been reviewed and compiled using benchmark datasets such as Indian Pines, Salinas Valley, Pavia University, and others. Finally, it is intended that the readers will gain a thorough understanding of existing segmentation techniques, their performance, and fresh research areas for HSI that need to be studied or explored.

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

Access this article

Log in via an institution

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32

Similar content being viewed by others

Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Akbari D (2020a) Improving spatial-spectral classification of hyperspectral imagery by using extended minimum spanning forest algorithm. Can J Remote Sens 46(2):146–153

    Google Scholar 

  2. Akbari D (2020b) A novel method for spectral-spatial classification of hyperspectral images with a high spatial resolution. Arab J Geosci 13(23):1–10

    Google Scholar 

  3. Angulo J, Velasco-Forero S, Chanussot J (2009) Multiscale stochastic watershed for unsupervised hyperspectral image segmentation. In: 2009 IEEE international geoscience and remote sensing symposium, vol 3, IEEE, pp III–93

  4. Appice A, Malerba D (2019) Segmentation-aided classification of hyperspectral data using spatial dependency of spectral bands. ISPRS J Photogramm Remote Sens 147:215–231

    Google Scholar 

  5. Azimpour P, Shad R, Ghaemi M, Etemadfard H (2020) Hyperspectral image clustering with albedo recovery fuzzy c-means. Int J Remote Sens 41(16):6117–6134

    Google Scholar 

  6. Beirami BA, Mokhtarzade M (2020) Band grouping superpca for feature extraction and extended morphological profile production from hyperspectral images. IEEE Geosci Remote Sens Lett 17(11):1953–1957

    Google Scholar 

  7. Benediktsson JA, Palmason JA, Sveinsson JR (2005) Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans Geosci Remote Sens 43(3):480–491

    Google Scholar 

  8. Binge C, Faxi Z, Xiudan M, Yanan W (2016) Hyperspectral image classification based on image segmentation. In: 2016 international conference on intelligent transportation, big data & Smart City (ICITBS), IEEE, pp 101–104

  9. Cao F, Guo W (2020) Deep hybrid dilated residual networks for hyperspectral image classification. Neurocomputing 384:170–181

    Google Scholar 

  10. Cao X, Lu H, Ren M, Jiao L (2019) Non-overlapping classification of hyperspectral imagery with superpixel segmentation. Appl Soft Comput 83:105630

    Google Scholar 

  11. Cao X, Wang D, Wang X, Zhao J, Jiao L (2020) Hyperspectral imagery classification with cascaded support vector machines and multi-scale superpixel segmentation. Int J Remote Sens 41(12):4530–4550

    Google Scholar 

  12. Cariou C, Chehdi K, Le Moan S (2020) Improved nearest neighbor density-based clustering techniques with application to hyperspectral images. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 4127–4131

  13. Castaings T, Waske B, Atli Benediktsson J, Chanussot J (2010) On the influence of feature reduction for the classification of hyperspectral images based on the extended morphological profile. Int J Remote Sens 31(22):5921–5939

    Google Scholar 

  14. Challa A, Danda S, Sagar B, Najman L (2021) Triplet-watershed for hyperspectral image classification. arXiv:2103.09384

  15. Chan T-H, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) Pcanet: A simple deep learning baseline for image classification? IEEE Trans Image Process 24(12):5017–5032

    MathSciNet  MATH  Google Scholar 

  16. Chauhan NS (2019) Introduction to image segmentation with k-means clustering. https://www.kdnuggets.com/2019/08/introduction-image-segmentation-k-means-clustering.html, Accessed December 2021

  17. Che W, Sun L, Zhang Q, Tan W, Ye D, Zhang D, Liu Y (2018) Pixel based bruise region extraction of apple using vis-nir hyperspectral imaging. Comput Electron Agric 146:12–21

    Google Scholar 

  18. Chen C, Jiang F, Yang C, Rho S, Shen W, Liu S, Liu Z (2018a) Hyperspectral classification based on spectral–spatial convolutional neural networks. Eng Appl Artif Intell 68:165–171

    Google Scholar 

  19. Chen C-W, Tseng Y-S, Mukundan A, Wang H-C (2021) Air pollution: Sensitive detection of pm2. 5 and pm10 concentration using hyperspectral imaging. Appl Sci 11(10):4543

    Google Scholar 

  20. Chen L, Chen CP, Lu M (2011) A multiple-kernel fuzzy c-means algorithm for image segmentation. IEEE Transactions on systems, man, and cybernetics, part B (Cybernetics) 41(5):1263–1274

    Google Scholar 

  21. Chen S, Sun T, Yang F, Sun H, Guan Y (2018b) An improved optimum-path forest clustering algorithm for remote sensing image segmentation. Comput Geosci 112:38–46

    Google Scholar 

  22. Cheng G, Li Z, Han J, Yao X, Guo L (2018) Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(11):6712–6722

    Google Scholar 

  23. Chu X, Wang W, Ni X, Li C, Li Y (2020) Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging. Infrared Phys Technol 105:103242

    Google Scholar 

  24. Cui B, Ma X, Xie X, Ren G, Ma Y (2017) Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering. Infrared Phys Technol 81:79–88

    Google Scholar 

  25. Das A, Bhardwaj K, Patra S, Bruzzone L (2020) A novel threshold detection technique for the automatic construction of attribute profiles in hyperspectral images. IEEE journal of selected topics in applied earth observations and remote sensing 13:1374–1384

    Google Scholar 

  26. Dutta T, Dey S, Bhattacharyya S (2020) Automatic clustering of hyperspectral images using qutrit based particle swarm optimization. In: Intelligence enabled research, Springer, pp 21–31

  27. Fei B (2020) Hyperspectral imaging in medical applications. In: Data handling in science and technology, vol 32, Elsevier, pp 523–565

  28. Gao Y, Cheng T, Wang B (2020a) Nonlinear anomaly detection based on spectral-spatial composite kernel for hyperspectral images. IEEE geoscience and remote sensing letters

  29. Gao Z, Shao Y, Xuan G, Wang Y, Liu Y, Han X (2020b) Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Comput Geosci 4:31–38

    Google Scholar 

  30. Ghamisi P, Benediktsson JA, Ulfarsson MO (2013a) Spectral–spatial classification of hyperspectral images based on hidden markov random fields. IEEE Trans Geosci Remote Sens 52(5):2565–2574

    Google Scholar 

  31. Ghamisi P, Couceiro MS, Benediktsson JA, Ferreira NM (2012) An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst Appl 39(16):12407–12417

    Google Scholar 

  32. Ghamisi P, Couceiro MS, Martins FM, Benediktsson JA (2013b) Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Transactions on Geoscience and Remote sensing 52(5):2382–2394

    Google Scholar 

  33. Głomb P, Romaszewski M, Cholewa M, Domino K (2018) Application of hyperspectral imaging and machine learning methods for the detection of gunshot residue patterns. Forensic Sci Int 290:227–237

    Google Scholar 

  34. Gonzalez R (2009) Digital Image Processing. Pearson Education

  35. Gu Y, Liu H (2016) Sample-screening mkl method via boosting strategy for hyperspectral image classification. Neurocomputing 173:1630–1639

    Google Scholar 

  36. Guo Y, Han S, Li Y, Zhang C, Bai Y (2018) K-nearest neighbor combined with guided filter for hyperspectral image classification. Procedia Comput Sci 129:159–165

    Google Scholar 

  37. Hang R, Liu Q, Hong D, Ghamisi P (2019) Cascaded recurrent neural networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(8):5384–5394

    Google Scholar 

  38. Hu L, Qi C, Wang Q (2018) Spectral-spatial hyperspectral image classification based on mathematical morphology post-processing. Procedia Comput Sci 129:93–97

    Google Scholar 

  39. Ismail M, Orlandić M (2020) Segment-based clustering of hyperspectral images using tree-based data partitioning structures. Algorithms 13(12):330

    MathSciNet  Google Scholar 

  40. Ji Y, Sun L, Li Y, Li J, Liu S, Xie X, Xu Y (2019a) Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine. Infrared Phys Technol 99:71–79

    Google Scholar 

  41. Ji Y, Sun L, Li Y, Ye D (2019b) Detection of bruised potatoes using hyperspectral imaging technique based on discrete wavelet transform. Infrared Phys Technol 103:103054

    Google Scholar 

  42. Jianxin Z, Kangping Z, Junkai W, Xudong H (2020) Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system. Textile Research Journal, pp 0040517520957401

  43. Jiao L, Shang R, Liu F, Zhang W (2020) Brain and Nature-inspired Learning, Computation and Recognition. Elsevier

  44. Kang X, Duan P, Li S (2020) Hyperspectral image visualization with edge-preserving filtering and principal component analysis. Inform Fusion 57:130–143

    Google Scholar 

  45. Kumar B, Dikshit O (2017) Hyperspectral image classification based on morphological profiles and decision fusion. Int J Remote Sens 38 (20):5830–5854

    Google Scholar 

  46. Li J, Chen L, Huang W (2018a) Detection of early bruises on peaches (amygdalus persica l.) using hyperspectral imaging coupled with improved watershed segmentation algorithm. Postharvest Biol Technol 135:104–113

    Google Scholar 

  47. Li J, Luo W, Wang Z, Fan S (2019a) Early detection of decay on apples using hyperspectral reflectance imaging combining both principal component analysis and improved watershed segmentation method. Postharvest Biol Technol 149:235–246

    Google Scholar 

  48. Li J, Zhang R, Li J, Wang Z, Zhang H, Zhan B, Jiang Y (2019b) Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method. Postharvest Biol Technol 158:110986

    Google Scholar 

  49. Li X, Li R, Wang M, Liu Y, Zhang B, Zhou J (2018b) Hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables. In: Hyperspectral imaging in agriculture, food and environment. IntechOpen Limited London, UK, pp 27–63

  50. Li Y, Xie W, Li H (2017) Hyperspectral image reconstruction by deep convolutional neural network for classification. Pattern Recogn 63:371–383

    Google Scholar 

  51. Lima C, Correa L, Byrne H, Zezell D (2018) K-means and hierarchical cluster analysis as segmentation algorithms of ftir hyperspectral images collected from cutaneous tissue. In: 2018 SBFoton international optics and photonics conference (SBFoton IOPC), IEEE, pp 1–4

  52. Lin L, Zhang S (2020) Superpixel segmentation of hyperspectral images based on entropy and mutual information. Appl Sci 10(4):1261

    Google Scholar 

  53. Lin Z, Chen Y, Zhao X, Wang G (2013) Spectral-spatial classification of hyperspectral image using autoencoders. In: 2013 9th international conference on information, communications & signal processing, IEEE, pp 1–5

  54. Liu Y, Cao G, Sun Q, Siegel M (2015) Hyperspectral classification via deep networks and superpixel segmentation. Int J Remote Sens 36 (13):3459–3482

    Google Scholar 

  55. Liu Z, Jiang J, Qiao X, Qi X, Pan Y, Pan X (2020) Using convolution neural network and hyperspectral image to identify moldy peanut kernels. LWT 132:109815

    Google Scholar 

  56. Lu J, Liu H, Yao Y, Tao S, Tang Z, Lu J (2020) Hsi road: a hyper spectral image dataset for road segmentation. In: 2020 IEEE international conference on multimedia and expo (ICME), IEEE, pp 1–6

  57. Lu Q, Hu X (2020) Hyperspectral image classification via exploring spectral–spatial information of saliency profiles. IEEE Journal of selected topics in applied earth observations and remote sensing 13:3291–3303

    Google Scholar 

  58. Lv ZY, Zhang P, Benediktsson JA, Shi WZ (2014) Morphological profiles based on differently shaped structuring elements for classification of images with very high spatial resolution. IEEE Journal of selected topics in applied earth observations and remote sensing 7(12):4644–4652

    Google Scholar 

  59. Mehta A, Dikshit O (2016) Projected clustering of hyperspectral imagery using region merging. Remote Sens Lett 7(8):721–730

    Google Scholar 

  60. Mehta A, Dikshit O (2017) Segmentation-based clustering of hyperspectral images using local band selection. J Appl Remote Sens 11(1):015028

    Google Scholar 

  61. Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: A survey. IEEE Transactions on pattern analysis and machine intelligence

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

    Google Scholar 

  63. Myasnikov EV (2017) Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches. 41(4)

  64. Nalepa J, Antoniak M, Myller M, Lorenzo PR, Marcinkiewicz M (2020) Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation. Microprocess Microsyst 73:102994

    Google Scholar 

  65. Noviyanto A, Abdulla WH (2019) Segmentation and calibration of hyperspectral imaging for honey analysis. Comput Electron Agric 159:129–139

    Google Scholar 

  66. Pan B, Shi Z, Xu X (2018) Mugnet: Deep learning for hyperspectral image classification using limited samples. ISPRS J Photogramm Remote Sens 145:108–119

    Google Scholar 

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

    Google Scholar 

  68. Peter Protzel AL (2015) Superpixels and their application for visual place recognition in changing environments. https://nbn-resolving.org/urn:nbn:de:bsz:ch1-qucosa-190241, Accessed December 2021

  69. Pisani RJ, Nakamura RYM, Riedel PS, Zimback CRL, Falcao AX, Papa JP (2014) Toward satellite-based land cover classification through optimum-path forest. IEEE Trans Geosci Remote Sens 52(10):6075–6085

    Google Scholar 

  70. Qiao X, Jiang J, Qi X, Guo H, Yuan D (2017) Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts. Food Chem 220:393–399

    Google Scholar 

  71. Quesada-Barriuso P, Heras DB, Argüello F (2021) Gpu accelerated waterpixel algorithm for superpixel segmentation of hyperspectral images. The Journal of Supercomputing, pp 1–13

  72. Kulkarni SA, Kamathe R (2014) Mri brain image segmentation by edge detection and region selection

  73. Song J, Hu M, Wang J, Zhou M, Sun L, Qiu S, Li Q, Sun Z, Wang Y (2019) Alk positive lung cancer identification and targeted drugs evaluation using microscopic hyperspectral imaging technique. Infrared Phys Technol 96:267–275

    Google Scholar 

  74. Stéfan van der W, Johannes L, Schönberger JN-IFBJDWNYEGTY (2014a) Image manipulation and processing using numpy and scipy. https://scipy-lectures.org/advanced/image_processing/, Accessed December 2021

  75. Stéfan van der W, Johannes L, Schönberger J.N.-I.F.B.J.D.W.N.Y.E.G.T.Y (2014b) Image manipulation and processing using numpy and scipy. https://scipy-lectures.org/advanced/image_processing/, Accessed December 2021

  76. Sudakov I, Essa A, Mander L, Gong M, Kariyawasam T (2017) The geometry of large tundra lakes observed in historical maps and satellite images. Remote Sens 9(10):1072

    Google Scholar 

  77. Tarabalka Y, Chanussot J, Benediktsson JA (2010a) Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recogn 43(7):2367–2379

    MATH  Google Scholar 

  78. Tarabalka Y, Fauvel M, Chanussot J, Benediktsson JA (2010b) Svm-and mrf-based method for accurate classification of hyperspectral images. IEEE Geosci Remote Sens Lett 7(4):736–740

    Google Scholar 

  79. Tarabalka Y, Tilton JC (2012) Improved hierarchical optimization-based classification of hyperspectral images using shape analysis. In: 2012 IEEE international geoscience and remote sensing symposium, IEEE, pp 1409–1412

  80. Tian X, Fan S, Huang W, Wang Z, Li J (2020) Detection of early decay on citrus using hyperspectral transmittance imaging technology coupled with principal component analysis and improved watershed segmentation algorithms. Postharvest Biol Technol 161:111071

    Google Scholar 

  81. Tian X, Li J, Wang Q, Fan S, Huang W, Zhao C (2019) A multi-region combined model for non-destructive prediction of soluble solids content in apple, based on brightness grade segmentation of hyperspectral imaging. Biosystems Eng 183:110–120

    Google Scholar 

  82. Torres I, Sánchez M-T, Cho B-K, Garrido-Varo A, Pérez-marín D (2019) Setting up a methodology to distinguish between green oranges and leaves using hyperspectral imaging. Comput Electron Agric 167:105070

    Google Scholar 

  83. Torti E, Florimbi G, Castelli F, Ortega S, Fabelo H, Callicó GM, Marrero-Martin M, Leporati F (2018) Parallel k-means clustering for brain cancer detection using hyperspectral images. Electronics 7(11):283

    Google Scholar 

  84. Tu B, Li N, Fang L, Fei H, He D (2018) Classification of hyperspectral images via weighted spatial correlation representation. J Vis Commun Image Represent 56:160–166

    Google Scholar 

  85. Vaddi R, Manoharan P (2020) Hyperspectral image classification using cnn with spectral and spatial features integration. Infrared Phys Technol, pp 103296

  86. Verma H, Agrawal R, Sharan A (2016) An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation. Appl Soft Comput 46:543–557

    Google Scholar 

  87. Wan X, Zhao C (2019) Spectral-spatial hyperspectral image classification combining multi-scale bi-exponential edge-preserving filtering and susan edge detector. Infrared Phys Technol 102:103055

    Google Scholar 

  88. Wang H, Li W, Huang W, Niu J, Nie K (2020a) Research on land use classification of hyperspectral images based on multiscale superpixels. Math Biosci Eng 17(5):5099–5119

    MATH  Google Scholar 

  89. Wang Y, Yu W, Fang Z (2020b) Multiple kernel-based svm classification of hyperspectral images by combining spectral, spatial, and semantic information. Remote Sens 12(1):120

    Google Scholar 

  90. Wang Y-J, Jin G, Li L-Q, Liu Y, Kalkhajeh YK, Ning J-M, Zhang Z-Z (2020c) Nir hyperspectral imaging coupled with chemometrics for nondestructive assessment of phosphorus and potassium contents in tea leaves. Infrared Phys Technol 108:103365

    Google Scholar 

  91. Wu Y, Hu B, Gao X, Wei R (2018) Hyperspectral image classification based on adaptive segmentation. Optik 172:612–621

    Google Scholar 

  92. Xia J, Bombrun L, Adalı T, Berthoumieu Y, Germain C (2016) Spectral–spatial classification of hyperspectral images using ica and edge-preserving filter via an ensemble strategy. IEEE Transactions on geoscience and remote sensing 54(8):4971–4982

    Google Scholar 

  93. Ye D, Sun L, Tan W, Che W, Yang M (2018) Detecting and classifying minor bruised potato based on hyperspectral imaging. Chemometr Intell Lab Syst 177:129–139

    Google Scholar 

  94. Yijie W, CHENG J (2018) Rapid and non-destructive prediction of protein content in peanut varieties using near-infrared hyperspectral imaging method. Grain & Oil Sci Technol 1(1):40–43

    Google Scholar 

  95. Youn S, Lee C (2013) Edge detection for hyperspectral images using the bhattacharyya distance. In: 2013 international conference on parallel and distributed systems, IEEE, pp 716–719

  96. Yu H, Gao L, Liao W, Zhang B, Pižurica A, Philips W (2017) Multiscale superpixel-level subspace-based support vector machines for hyperspectral image classification. IEEE Geosci Remote Sens Lett 14(11):2142–2146

    Google Scholar 

  97. Zabalza J, Ren J, Zheng J, Zhao H, Qing C, Yang Z, Du P, Marshall S (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185:1–10

    Google Scholar 

  98. Zeng S, Wang Z, Huang R, Chen L, Feng D (2019) A study on multi-kernel intuitionistic fuzzy c-means clustering with multiple attributes. Neurocomputing 335:59–71

    Google Scholar 

  99. Zhang L, Sun H, Rao Z, Ji H (2020) Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds. Spectrochimica Acta Part A: Mol Biomol Spectrosc 229:117973

    Google Scholar 

  100. Zhang Y, Kang X, Li S, Duan P, Benediktsson JA (2019a) Feature extraction from hyperspectral images using learned edge structures. Remote Sensing Lett 10(3):244–253

    Google Scholar 

  101. Zhang Y, Liu K, Dong Y, Wu K, Hu X (2019b) Semisupervised classification based on slic segmentation for hyperspectral image. IEEE Geosci Remote Sens Lett 17(8):1440–1444

    Google Scholar 

  102. Zhang Y, Wu L, Deng L, Ouyang B (2021) Retrieval of water quality parameters from hyperspectral images using a hybrid feedback deep factorization machine model. Water Res 204:117618

    Google Scholar 

  103. Zhou F, Hang R, Liu Q, Yuan X (2019a) Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328:39–47

    Google Scholar 

  104. Zhou P, Han J, Cheng G, Zhang B (2019b) Learning compact and discriminative stacked autoencoder for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(7):4823–4833

    Google Scholar 

  105. Zhou S, Sun L, Xing W, Feng G, Ji Y, Yang J, Liu S (2020) Hyperspectral imaging of beet seed germination prediction. Infrared Physics & Technology, pp 103363

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology Patiala, Punjab, India

    Reaya Grewal, Singara Singh Kasana & Geeta Kasana

Authors
  1. Reaya Grewal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Singara Singh Kasana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geeta Kasana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Singara Singh Kasana.

Ethics declarations

Conflict of Interests

The authors declare that there is no conflicts 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

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grewal, R., Kasana, S.S. & Kasana, G. Hyperspectral image segmentation: a comprehensive survey. Multimed Tools Appl 82, 20819–20872 (2023). https://doi.org/10.1007/s11042-022-13959-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13959-w

Keywords

Search

Navigation