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Workshop on Computer Vision Applications

WCVA 2018: Computer Vision Applications pp 115–128Cite as

AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral Image Classification

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1019))

Abstract

This paper addresses an approach for classification of hyperspectral imagery (HSI). In remote sensing, the HSI sensor acquires hundreds of images with very narrow but continuous spectral width in visible and near-infrared regions of the electromagnetic (EM) spectrum. Due to the nature of data acquisition with contiguous bands, the HS images are very useful in classification and/or the identification of materials present in the captured geographical area. However, the low spatial resolution and large volume of HS images make the classification of those images as a challenging task. In the proposed approach, we use an autoencoder with convolutional neural network (AECNN) for classification of HS image. Pre-processing procedure with autoencoder leads to obtain optimized weights in the initial layer of CNN model. Moreover, features are enhanced in the HS images by utilizing the autoencoder. The CNN is used for efficient extraction of the features and same is also utilised for the classification of HS data. The potential of the proposed approach has been verified by conducting the experiments on three recent datasets. The experimental results are compared with the results obtained in the geoscience and remote sensing society (GRSS) Image Fusion Contest-2018 held at IEEE International Geoscience and Remote Sensing Symposium (IGARSS)-2018 and other existing frameworks for HSI classification. The testing accuracy of classification for the proposed approach is better than that of the other existing deep learning based methods.

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References

  1. Aptoula, E., Ozdemir, M.C., Yanikoglu, B.: Deep learning with attribute profiles for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 13(12), 1970–1974 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Camps-Valls, G., Tuia, D., Bruzzone, L., Benediktsson, J.A.: Advances in hyperspectral image classification: earth monitoring with statistical learning methods. IEEE Signal Process. Mag. 31(1), 45–54 (2014)

    Article  Google Scholar 

  4. Chen, X., Xiang, S., Liu, C.L., Pan, C.H.: Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett 11(10), 1797–1801 (2014)

    Article  Google Scholar 

  5. Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49(10), 3973–3985 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Ding, C., Xu, C., Tao, D.: Multi-task pose-invariant face recognition. IEEE Trans. Image Process. 24(3), 980–993 (2015)

    Article  MathSciNet  Google Scholar 

  9. Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.: Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 43(3), 492–501 (2005)

    Article  Google Scholar 

  10. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)

    Google Scholar 

  11. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)

    Google Scholar 

  12. Kingma, D.P., Ba, J.L.: Adam: A method for stochastic optimization. In: Proceedings of 3rd International Conference for Learning Representations, pp. 1–15 (2015)

    Google Scholar 

  13. Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)

    Article  Google Scholar 

  14. Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Trans. Geosci. Remote Sens. 50(3), 809–823 (2012)

    Article  Google Scholar 

  15. Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Geosci. Remote Sens. Lett. 10(2), 318–322 (2013)

    Article  Google Scholar 

  16. Li, W., Wu, G., Zhang, F., Du, Q.: Hyperspectral image classification using deep pixel-pair features. IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017)

    Article  Google Scholar 

  17. Liu, W., Mei, T., Zhang, Y., Che, C., Luo, J.: Multi-task deep visual-semantic embedding for video thumbnail selection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3707–3715 (2015)

    Google Scholar 

  18. Luus, F.P., Salmon, B.P., Van den Bergh, F., Maharaj, B.T.J.: Multiview deep learning for land-use classification. IEEE Geosci. Remote Sens. Lett. 12(12), 2448–2452 (2015)

    Article  Google Scholar 

  19. Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol. 30, p. 3 (2013)

    Google Scholar 

  20. Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962. IEEE (2015)

    Google Scholar 

  21. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)

    Google Scholar 

  22. Pal, M., Foody, G.M.: Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 48(5), 2297–2307 (2010)

    Article  Google Scholar 

  23. Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)

    Google Scholar 

  24. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  25. Windrim, L., Melkumyan, A., Murphy, R.J., Chlingaryan, A., Ramakrishnan, R.: Pretraining for hyperspectral convolutional neural network classification. IEEE Trans. Geosci. Remote Sens. 56, 2798–2810 (2018)

    Article  Google Scholar 

  26. Xu, X., Li, W., Ran, Q., Du, Q., Gao, L., Zhang, B.: Multisource remote sensing data classification based on convolutional neural network. IEEE Trans. Geosci. Remote Sens. 56(2), 937–949 (2018)

    Article  Google Scholar 

  27. Yan, D., Chu, Y., Li, L., Liu, D.: Hyperspectral remote sensing image classification with information discriminative extreme learning machine. Multimed. Tools Appl. 77(5), 5803–5818 (2018)

    Article  Google Scholar 

  28. Yu, S., Jia, S., Xu, C.: Convolutional neural networks for hyperspectral image classification. Neurocomputing 219, 88–98 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Zhao, W., Guo, Z., Yue, J., Zhang, X., Luo, L.: On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery. Int. J. Remote Sens. 36(13), 3368–3379 (2015)

    Article  Google Scholar 

  32. Zhu, C., Peng, Y.: A boosted multi-task model for pedestrian detection with occlusion handling. IEEE Trans. Image Process. 24(12), 5619–5629 (2015)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

    Heena Patel & Kishor P. Upla

Authors
  1. Heena Patel
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  2. Kishor P. Upla
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Corresponding author

Correspondence to Kishor P. Upla .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India

    Chetan Arora

  2. Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India

    Kaushik Mitra

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Cite this paper

Patel, H., Upla, K.P. (2019). AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral Image Classification. In: Arora, C., Mitra, K. (eds) Computer Vision Applications. WCVA 2018. Communications in Computer and Information Science, vol 1019. Springer, Singapore. https://doi.org/10.1007/978-981-15-1387-9_10

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  • DOI: https://doi.org/10.1007/978-981-15-1387-9_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1386-2

  • Online ISBN: 978-981-15-1387-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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