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Resnet based hybrid convolution LSTM for hyperspectral image classification

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Abstract

There are many spectral bands of different wavelengths present in Hyperspectral Image containing a huge amount of information that helps to detect and identify various objects. Many challenges are faced at the time of analyzing a hyperspectral image like information loss, hindrances posed by redundant information lingering on input data and the presence of high dimensions, etc. In this paper, we proposed a Resnet ConvLSTM model which is composed of a 2D Convolution Neural Network together with Batch Normalization and it helps to minimize the computational complexity and to extract features from Hyperspectral Image. At the same time, we added skip connections to eliminate the vanishing gradient problem, being followed by the Long Short Term Memory layer to remove redundant information from an input image. We implemented our model on three different types of hyperspectral data sets and also on three different types of time series data sets. Our model produced better accuracy than others’ proposed models reaching the levels of 0.07%, 0.01%, and 0.56% more in the "Indian Pines", "Pavia University", and "Botswana" data sets respectively. The commitment of our errors decreased in time series datasets by 0.44, 0.08, and 0.5 in "Electricity production", "International Airline Passenger" and "Production of shampoo over three years" respectively. The source code is available at https://github.com/Anasua-coding/HSI-Classification/tree/main.

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References

  1. Cai Y, Zhang Z, Liu X, Cai Z (2020) Efficient graph convolutional self-representation for band selection of hyperspectral image. In: IEEE journal of selected topics in applied earth observations and remote sensing, vol 13, pp 4869–4880. https://doi.org/10.1109/JSTARS.2020.3018229

  2. Signoroni A, Savardi M, Baronio A, Benini S (2019) Deep learning meets hyperspectral image analysis: a multidisciplinary review. J Imaging 5(5):52

    Article  Google Scholar 

  3. Mou L, Ghamisi P, Zhu XX (2017) Deep recurrent neural networks for hyperspectral image classification. In: IEEE transactions on geoscience and remote sensing, vol 55, no 7, pp 3639–3655. https://doi.org/10.1109/TGRS.2016.2636241

  4. 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, pp 770–778

  5. Amy L, Harrison N, French AP (2017) Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant methods 13(1):1–12

    Google Scholar 

  6. Gao P, Zhang H, Jia D, Song C, Cheng C, Shen S (2020) Efficient approach for computing the discrimination ratio-based variant of information entropy for image processing. In IEEE Access 8:92552–92564. https://doi.org/10.1109/ACCESS.2020.2994345

    Article  Google Scholar 

  7. Roy SK, Krishna G, Dubey SR, Chaudhuri BB (2020) HybridSN: exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification. In: IEEE geoscience and remote sensing letters, vol 17, no 2, pp 277–281. https://doi.org/10.1109/LGRS.2019.2918719

  8. Hu WS, Li HC, Pan L, Li W, Tao R, Qian D (2019) Feature extraction and classification based on spatial-spectral convlstm neural network for hyperspectral images. arXiv preprint arXiv:1905.03577

  9. Liu Q, Zhou F, Hang R, Yuan X (2017) Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12):1330

    Article  Google Scholar 

  10. (2009) ISPRS J Photo Remote Sen, 1995–Present

  11. Alam FI, Zhou J, Liew AW-C, Jia X, Chanussot J, Gao Y (2019) Conditional random field and deep feature learning for hyperspectral image classification. In: IEEE transactions on geoscience and remote sensing, vol 57, no 3, pp 1612–1628. https://doi.org/10.1109/TGRS.2018.2867679

  12. Weinmann M, Weidner U (2019) Relevance assessment of spectral bands for land cover and land use classification: a case study involving multispectral sentinel-2-like and hyperspectral data. TP Kersten (Hrsg.) 39(2019):138–153

    Google Scholar 

  13. Lorenzo PR, Tulczyjew L, Marcinkiewicz M, Nalepa J (2018) Band selection from hyperspectral images using attention-based convolutional neural networks. arXiv preprint arXiv:1811.02667

  14. Banik D, Ekbal A, Bhattacharyya P, Bhattacharyya S (2019) Assembling translations from multi-engine machine translation outputs. Appl Soft Comput 78(2019):230–239

    Article  Google Scholar 

  15. Banik D, Ekbal A, Bhattacharyya P (2019) Machine learning based optimized pruning approach for decoding in statistical machine translation. In IEEE Access 7:1736–1751. https://doi.org/10.1109/ACCESS.2018.2883738

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Audebert N, Le Saux B, Lefévre S (2019) Deep learning for classification of hyperspectral data: a comparative review. IEEE Geosci Remote Sensing Magazine 7(2):159–173

    Article  Google Scholar 

  18. Peeples J, Xu W, Zare A (2022) Histogram layers for texture analysis. In: IEEE transactions on artificial intelligence, vol 3, no 4, pp 541–552. https://doi.org/10.1109/TAI.2021.3135804

  19. Yang X, Ye Y, Li X, Lau RYK, Zhang X, Huang X (2018) Hyperspectral image classification with deep learning models. In: IEEE transactions on geoscience and remote sensing, vol 56, no 9, pp 5408–5423. https://doi.org/10.1109/TGRS.2018.2815613.

  20. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. In: IEEE transactions on geoscience and remote sensing, vol 42, no 8, pp 1778–1790. https://doi.org/10.1109/TGRS.2004.831865

  21. Cai Y, Liu X, Cai Z (2020) BS-Nets: an end-to-end framework for band selection of hyperspectral image. In: IEEE transactions on geoscience and remote sensing, vol 58, no 3, pp 1969–1984. https://doi.org/10.1109/TGRS.2019.2951433

  22. Roy SK, Chatterjee S, Bhattacharyya S, Chaudhuri BB, Platoš dJ (2020) Lightweight spectral-spatial squeeze-and- excitation residual bag-of-features learning for hyperspectral classification. In: IEEE transactions on geoscience and remote sensing, vol 58, no 8, pp 5277–5290. https://doi.org/10.1109/TGRS.2019.2961681

  23. Wenju W, Dou S, Wang S (2019) Alternately updated spectral-spatial convolution network for the classification of hyperspectral images. Remote Sensing 11(15):1794

    Article  Google Scholar 

  24. Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. In: IEEE transactions on geoscience and remote sensing, vol 54, no 10, pp 6232–6251. https://doi.org/10.1109/TGRS.2016.2584107

  25. Waske B, van der Linden S, Benediktsson JA, Rabe A, Hostert P (2010) Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. In: IEEE transactions on geoscience and remote sensing, vol 48, no 7, pp 2880–2889. https://doi.org/10.1109/TGRS.2010.2041784

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

  27. Zhong Z, Li J, Luo Z, Chapman M (2018) Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. In: IEEE transactions on geoscience and remote sensing, vol 56, no 2, pp 847–858. https://doi.org/10.1109/TGRS.2017.2755542

  28. Ben Hamida A, Benoit A, Lambert P, Ben Amar C (2018) 3-D deep learning approach for remote sensing image classification. In: IEEE transactions on geoscience and remote sensing, vol 56, no 8, pp 4420–4434. https://doi.org/10.1109/TGRS.2018.2818945

  29. Fejjari A, Ettabaa KS, Korbaa O (2021) Chapter 12 feature extraction techniques for hyperspectral images classification

  30. Lingyu Y, Li K, Gao R, Wang C, Xiong N (2022) An intelligent weighted object detector for feature extraction to enrich global image information. Appl Sci 12(15):7825

    Article  Google Scholar 

  31. Lingyu Y, Fu J, Wang C, Ye Z, Chen H, Ling H (2021) Enhanced network optimized generative adversarial network for image enhancement. Multimed Tools Appl 80(2021):14363–14381

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering, National Institute of Technology, Jamshedpur, Adityapur, Jharkhand, 831014, India

    Anasua Banerjee

  2. School of Computer Science & Artificial Intelligence, SR University, Warangal, Hasanparthy, Telangana, 506371, India

    Debajyoty Banik

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  1. Anasua Banerjee
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  2. Debajyoty Banik
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Correspondence to Debajyoty Banik.

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Anasua Banerjee and Debajyoty Banik are contributed equally to this work.

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Banerjee, A., Banik, D. Resnet based hybrid convolution LSTM for hyperspectral image classification. Multimed Tools Appl 83, 45059–45070 (2024). https://doi.org/10.1007/s11042-023-16241-9

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  • DOI: https://doi.org/10.1007/s11042-023-16241-9

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