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SSNET: an improved deep hybrid network for hyperspectral image classification

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Abstract

Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral–spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI.

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  1. www.ehu.eus/ccwintco/index.php/HyperspectralSensingScenes

References

  1. Chang C (2007) Hyperspectral data exploitation: theory and applications. Wiley, New York

    Book  Google Scholar 

  2. Ghamisi P, Yokoya N, Li J et al (2017) Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art. IEEE Geosci Remote Sens Mag 5:37–78. https://doi.org/10.1109/MGRS.2017.2762087

    Article  Google Scholar 

  3. Mishra NB, Crews KA (2014) Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with random forest. Int J Remote Sens 35:1175–1198. https://doi.org/10.1080/01431161.2013.876120

    Article  Google Scholar 

  4. Cheng G, Han J, Lu X (2017) Remote sensing image scene classification: benchmark and state of the art. Proc IEEE 105:1865–1883. https://doi.org/10.1109/JPROC.2017.2675998

    Article  Google Scholar 

  5. Chen Y, Liu L, Gong Z, Zhong P (2017) Learning CNN to pair UAV video image patches. IEEE J Sel Top Appl Earth Obs Remote Sens 10:5752–5768. https://doi.org/10.1109/JSTARS.2017.2740898

    Article  Google Scholar 

  6. Horig B, Kuhn F, Oschutz F, Lehmann F (2001) HyMap hyperspectral remote sensing to detect hydrocarbons. Int J Remote Sens 22:1413–1422. https://doi.org/10.1080/01431160120909

    Article  Google Scholar 

  7. Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 International geoscience and remote sensing symposium. pp 4959–4962. https://doi.org/10.1109/IGARSS.2015.7326945

  8. Lixin G, Weixin X, Jihong P (2015) Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images. Pattern Recognit 48:3216–3226. https://doi.org/10.1016/j.patcog.2015.04.013

    Article  Google Scholar 

  9. Camps-valls G, Tuia D, Bruzzone L, Benediktsson JA (2013) Advances in hyperspectral image classification. IEEE Signal Process Mag 31:45–54. https://doi.org/10.1109/MSP.2013.2279179

    Article  Google Scholar 

  10. Yang J, Qian J (2018) Hyperspectral image classification via multiscale joint collaborative representation with locally adaptive dictionary. IEEE Geosci Remote Sens Lett 15:112–116. https://doi.org/10.1109/lgrs.2017.2776113

    Article  Google Scholar 

  11. Fang L, He N, Li S et al (2018) A new spatial-spectral feature extraction method for hyperspectral images using local covariance matrix representation. IEEE Trans Geosci Remote Sens 56:3534–3546. https://doi.org/10.1109/TGRS.2018.2801387

    Article  Google Scholar 

  12. Fu Z, Qin Q, Luo B et al (2019) A local feature descriptor based on combination of structure and texture information for multispectral image matching. IEEE Geosci Remote Sens Lett 16:100–104. https://doi.org/10.1109/LGRS.2018.2867635

    Article  Google Scholar 

  13. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42:1778–1790. https://doi.org/10.1109/TGRS.2004.831865

    Article  Google Scholar 

  14. Ham JS, Chen Y, Crawford MM, Ghosh J (2005) Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans Geosci Remote Sens 43:492–501. https://doi.org/10.1109/TGRS.2004.842481

    Article  Google Scholar 

  15. Marpu PR, Pedergnana M, Dalla Mura M et al (2013) Automatic generation of standard deviation attribute profiles for spectral-spatial classification of remote sensing data. IEEE Geosci Remote Sens Lett 10:293–297. https://doi.org/10.1109/LGRS.2012.2203784

    Article  Google Scholar 

  16. Kang X, Li S, Benediktsson JA (2013) Spectral–spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans Geosci Remote Sens 52:2666–2677. https://doi.org/10.1109/TGRS.2013.2264508

    Article  Google Scholar 

  17. Kang X, Li S, Fang L et al (2015) Extended random walker-based classification of hyperspectral images. IEEE Trans Geosci Remote Sens 53:144–153. https://doi.org/10.1109/TGRS.2014.2319373

    Article  Google Scholar 

  18. Li S, Song W, Fang L et al (2019) Deep learning for hyperspectral image classification: an overview. IEEE Trans Geosci Remote Sens 57:6690–6709. https://doi.org/10.1109/TGRS.2019.2907932

    Article  Google Scholar 

  19. Shi C, Pun CM (2018) Superpixel-based 3D deep neural networks for hyperspectral image classification. Pattern Recognit 74:600–616. https://doi.org/10.1016/j.patcog.2017.09.007

    Article  Google Scholar 

  20. Nogueira K, Penatti OAB, dos Santos JA (2017) Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognit 61:539–556. https://doi.org/10.1016/j.patcog.2016.07.001

    Article  Google Scholar 

  21. Ribeiro M, Lazzaretti AE, Lopes HS (2018) A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognit Lett 105:13–22. https://doi.org/10.1016/j.patrec.2017.07.016

    Article  Google Scholar 

  22. Salakhutdinov R, Hinton G (2009) Deep Boltzmann machines. J Mach Learn Res 5:448–455

    MATH  Google Scholar 

  23. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527

    Article  MathSciNet  MATH  Google Scholar 

  24. Paoletti ME, Haut JM, Fernandez-Beltran R et al (2019) Capsule networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57:2145–2160. https://doi.org/10.1109/TGRS.2018.2871782

    Article  Google Scholar 

  25. Niu Z, Liu W, Zhao J, Jiang G (2019) DeepLab-based spatial feature extraction for hyperspectral image classification. IEEE Geosci Remote Sens Lett 16:251–255. https://doi.org/10.1109/LGRS.2018.2871507

    Article  Google Scholar 

  26. Paoletti ME, Haut JM, Fernandez-Beltran R et al (2019) Deep pyramidal residual networks for spectral-spatial hyperspectral image classification. IEEE Trans Geosci Remote Sens 57:740–754. https://doi.org/10.1109/TGRS.2018.2860125

    Article  Google Scholar 

  27. Ma X, Fu A, Wang J et al (2018) Hyperspectral image classification based on deep deconvolution network with skip architecture. IEEE Trans Geosci Remote Sens 56:4781–4791. https://doi.org/10.1109/IGARSS.2016.7729850

    Article  Google Scholar 

  28. Li Y, Xie W, Li H (2017) Hyperspectral image reconstruction by deep convolutional neural network for classification. Pattern Recognit 63:371–383. https://doi.org/10.1016/j.patcog.2016.10.019

    Article  Google Scholar 

  29. Chen Y, Li C, Ghamisi P et al (2017) Deep fusion of remote sensing data for accurate classification. IEEE Geosci Remote Sens Lett 14:1253–1257. https://doi.org/10.1109/LGRS.2017.2704625

    Article  Google Scholar 

  30. He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision. pp 2980–2988. https://doi.org/10.1109/ICCV.2017.322

  31. Kang X, Zhuo B, Duan P (2019) Dual-path network-based hyperspectral image classification. IEEE Geosci Remote Sens Lett 16:447–451. https://doi.org/10.1109/LGRS.2018.2873476

    Article  Google Scholar 

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

  33. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). pp 2261–2269. https://doi.org/10.1109/CVPR.2017.243

  34. Chen Y, Zhao X, Jia X (2015) Spectral-spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Top Appl Earth Obs Remote Sens 8:2381–2392. https://doi.org/10.1109/JSTARS.2015.2388577

    Article  Google Scholar 

  35. Chen Y, Zhu L, Ghamisi P et al (2017) Hyperspectral images classification with gabor filtering and convolutional neural network. IEEE Geosci Remote Sens Lett 14:2355–2359. https://doi.org/10.1109/LGRS.2017.2764915

    Article  Google Scholar 

  36. Zhong Z, Li J, Luo Z, Chapman M (2018) Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Trans Geosci Remote Sens 56:847–858. https://doi.org/10.1109/TGRS.2017.2755542

    Article  Google Scholar 

  37. Roy SK, Krishna G, Dubey SR, Chaudhuri BB (2019) HybridSN: exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geosci Remote Sens Lett 17:277–281. https://doi.org/10.1109/lgrs.2019.2918719

    Article  Google Scholar 

  38. Yue J, Mao S, Li M (2016) A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sens Lett 7:875–884. https://doi.org/10.1080/2150704X.2016.1193793

    Article  Google Scholar 

  39. Li N, Wang C, Zhao H et al (2018) A novel deep convolutional neural network for spectral-spatial classification of hyperspectral data. Int Arch Photogramm Remote Sens Spat Inf Sci - ISPRS Arch 42:897–900. https://doi.org/10.5194/isprs-archives-XLII-3-897-2018

    Article  Google Scholar 

  40. Hu W, Huang Y, Wei L et al (2015) Deep convolutional neural networks for hyperspectral image classification. J Sens 2015:1–12. https://doi.org/10.1155/2015/258619

    Article  Google Scholar 

  41. Khan S, Rahmani H, Shah SAA, Bennamoun M (2018) A Guide to convolutional neural networks for computer vision. Morgan & Claypool Publishers, San Rafael

    Book  Google Scholar 

  42. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv Prepr arXiv14091556 1–14

  43. Han X, Zhong Y, Cao L, Zhang L (2017) Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens 9:848. https://doi.org/10.3390/rs9080848

    Article  Google Scholar 

  44. Ben Hamida A, Benoit A, Lambert P, Ben Amar C (2018) 3-D deep learning approach for remote sensing image classification. IEEE Trans Geosci Remote Sens 56:4420–4434. https://doi.org/10.1109/TGRS.2018.2818945

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the support of CGM RCs, NRSC, ISRO, and Head (applications), RRSC-East, NRSC, ISRO, for carrying out the present work. The authors also acknowledge the collaboration extended by VC, MAKAUT, towards the work.

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Authors and Affiliations

  1. Regional Remote Sensing Centre-East, ISRO, Kolkata, India

    Arati Paul

  2. Microelectronics and VLSI Technology, MAKAUT, Haringhata, West Bengal, India

    Sanghamita Bhoumik

  3. Computer Science and Engineering, University of Calcutta, Kolkata, India

    Nabendu Chaki

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  1. Arati Paul
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  2. Sanghamita Bhoumik
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  3. Nabendu Chaki
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Correspondence to Arati Paul.

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Paul, A., Bhoumik, S. & Chaki, N. SSNET: an improved deep hybrid network for hyperspectral image classification. Neural Comput & Applic 33, 1575–1585 (2021). https://doi.org/10.1007/s00521-020-05069-1

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