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Pooled hybrid-spectral for hyperspectral image classification

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Abstract

Hyperspectral image is composed of many spectral bands. Due to this reason many problems crop up in the picture. The Presence of high dimension, information loss, clinging redundant information in spectral bands etc hinder at the time of hyperspectral image classification. Here we proposed Resnet Spectral Spatial ConvLstm model which is composed of 3D Convolution Neural Network together with batch normalization layers in order to extract the spectral spatial features from hyperspectral image simultaneously we added shortcut connections to get rid of vanishing gradient problem which is followed by 2D Convolution Neural Network layers to reduce the computational complexity over and above that Long Short Term Memory layer removes redundant information from an input image. Our model produced better accuracy than others’ proposed models like reaching the levels of 1.62%, 0.71%, 0.16%, and 0.01% more in “kennedy space center”, “Botswana”, “Indian Pines” and “Pavia University” data sets respectively. The errors also decreased from time series data sets by 0.49 in “Electricity production”, 0.16 in “International Airline Passenger” and 0.52 in “Production of shampoo over three years” by using our proposed model. We have uploaded the source code here https://github.com/debajyoty/Pooled-Hybrid-Spectral-for-Hyperspectral-Image-Classification.git.

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

References

  1. Audebert N, Le Saux B, Lefevre S (2019) Deep learning for classification of hyperspectral data: a comparative review. IEEE Geosci Remote Sens Mag 7(2):159–173. https://doi.org/10.1109/MGRS.2019.2912563

    Article  Google Scholar 

  2. 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(8):4420–4434. https://doi.org/10.1109/TGRS.2018.2818945

    Article  Google Scholar 

  3. 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 

  4. Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251. https://doi.org/10.1109/TGRS.2016.2584107

    Article  Google Scholar 

  5. Fang L, Wang C, Li S, Benediktsson JA (2017) Hyperspectral image classification via Multiple-Feature-Based adaptive sparse representation. IEEE Trans Instrum Meas 66(7):1646–1657. https://doi.org/10.1109/TIM.2017.2664480

    Article  Google Scholar 

  6. Gao Q, Lim S, Jia X (2018) Hyperspectral image classification using joint sparse model and discontinuity preserving relaxation. IEEE Geosci Remote Sens Lett 15(1):78–82. https://doi.org/10.1109/LGRS.2017.2774253

    Article  Google Scholar 

  7. Guo Y, Yin X, Zhao X et al (2019) Hyperspectral image classification with SVM and guided filter. J Wireless Com Network:56. https://doi.org/10.1186/s13638-019-1346-z

  8. Hasanlou M, Samadzadegan F (2012) Comparative study of intrinsic dimensionality estimation and dimension reduction techniques on hyperspectral images using k-NN classifier. IEEE Geosci Remote Sens Lett 9(6):1046–1050. https://doi.org/10.1109/LGRS.2012.2189547

    Article  Google Scholar 

  9. Hu W-S, Li H-C, Pan L, Li W, Tao R, Du Q (2019) Feature extraction and classification based on spatial-spectral convlstm neural network for hyperspectral images. arXiv:1905.03577

  10. Hu P, Liu X, Cai Y, Cai Z (2019) Band selection of hyperspectral images using multiobjective optimization-based sparse self-representation. IEEE Geosci Remote Sens Lett 16(3):452–456. https://doi.org/10.1109/LGRS.2018.2872540

    Article  Google Scholar 

  11. Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35 (1):221–231. https://doi.org/10.1109/TPAMI.2012.59

    Article  Google Scholar 

  12. Liu Q, Zhou F, Hang R, Yuan X (2017) Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification. Remote Sens 9(12). https://doi.org/10.3390/rs9121330

  13. Liu R, Ning X, Cai W, Li G (2021) Multiscale dense cross-attention mechanism with covariance pooling for hyperspectral image scene classification. Mob Inf Syst 2021. https://doi.org/10.1155/2021/9962057

  14. Liu R, Cai W, Li G, Ning X, Jiang Y (2022) Hybrid dilated convolution guided feature filtering and enhancement strategy for hyperspectral image classification. IEEE Geosci Remote Sens Lett 19:1–5. https://doi.org/10.1109/LGRS.2021.3100407

    Article  Google Scholar 

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

  16. Mou L, Ghamisi P, Zhu XX (2017) Deep recurrent neural networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55 (7):3639–3655. https://doi.org/10.1109/TGRS.2016.2636241

    Article  Google Scholar 

  17. Pandey PC, Balzter H, Srivastava PK, Petropoulos GP, Bhattacharya B (2020) Future perspectives and challenges in hyperspectral remote sensing. Hyperspectral Remote Sens:429–439

  18. Paoletti ME, Haut JM, Plaza J, Plaza A (2019) Deep learning classifiers for hyperspectral imaging: a review. ISPRS J Photogramm Remote Sens 158:279–317. https://doi.org/10.1016/j.isprsjprs.2019.09.006

    Article  Google Scholar 

  19. Roy SK, Chatterjee S, Bhattacharyya S, Chaudhuri BB, Platoš J (2020) Lightweight spectral–spatial squeeze-and- excitation residual bag-of-features learning for hyperspectral classification. IEEE Trans Geosci Remote Sens 58(8):5277–5290. https://doi.org/10.1109/TGRS.2019.2961681

    Article  Google Scholar 

  20. Roy SK, Krishna G, Dubey SR, Chaudhuri BB (2020) HybridSN: exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geosci Remote Sens Lett 17(2):277–281. https://doi.org/10.1109/LGRS.2019.2918719

    Article  Google Scholar 

  21. Steyn TFJ, Mostert PG, De Meyer CF, Van Rensburg LRJ (2011) The effect of service failure and recovery on airline-passenger relationships: a comparison between South African and United States airline passengers

  22. Veit A, Wilber MJ, Belongie S (2016) Residual networks behave like ensembles of relatively shallow networks. Adv Neural Inf Process Syst 29

  23. 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. IEEE Trans Geosci Remote Sens 48(7):2880–2889. https://doi.org/10.1109/TGRS.2010.2041784

    Article  Google Scholar 

  24. 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(2):847–858. https://doi.org/10.1109/TGRS.2017.2755542

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Patia, Bhubaneswar, 751024, Odisha, India

    Anasua Banerjee & 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 contributed equally to this work.

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Banerjee, A., Banik, D. Pooled hybrid-spectral for hyperspectral image classification. Multimed Tools Appl 82, 10887–10899 (2023). https://doi.org/10.1007/s11042-022-13721-2

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