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Abstract

Today, the classification process is demanded for modern city planning, agriculture and environmental monitoring, and many other applications. The optimum classification degree is still insufficient so far. The present classification methods for remote-sensing images are grouped according to the features they use into: manual feature-based methods, unsupervised feature learning methods, and supervised feature learning methods. In recent times, the supervised deep learning approaches are extensively introduced in various remote-sensing applications, such as object detection and land use scene classification. In this article, an experiment is conducted using one of the widespread deep learning models, Convolution Neural Networks (CNNs), specifically, AlexNet architecture on a standard sounded hyper spectral dataset, Pavia University (PaviaU). The model achieved an overall accuracy of 91% ± 0.01. A comparison with other different techniques is also introduced.

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References

  1. NASA: What is a satellite? NASA Knows! (Grades 5–8) series (2014)

    Google Scholar 

  2. Zhang, L., Xia, G., Wu, T., Lin, L., Tai, X.: Deep learning for remote sensing image understanding. J. Sens. 2016, 1–2 (2016)

    Google Scholar 

  3. Yaniv, O., Isaac, A., Vladimir, F., Daniel, G., Adrian, S.: Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal. J. Imaging 5(1), 3 (2019)

    Google Scholar 

  4. Roy, B., Lawrence, T., Mahdi, N.: Spectral imaging using a commercial colour-filter array digital camera. In: The Fourteenth Triennial ICOM-CC Meeting, pp. 743–750 (2005)

    Google Scholar 

  5. Adam, C.W., Vincent, G.A., Everett, A.H.: Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sens. 4(6), 1671–1692 (2012)

    Article  Google Scholar 

  6. Jose, A.J.B., Pablo, J.Z., Lola, S., Elias, F.: Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 47(3), 722–738 (2009)

    Article  Google Scholar 

  7. Elhadi, A., Onisimo, M., Denis, R.: Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol. Manag. 18(3), 281–296 (2010)

    Article  Google Scholar 

  8. Shaheera, R., Nicolas, D.: A split-and-merge approach for hyperspectral band selection. IEEE Geosci. Remote Sens. Lett. 14(8), 1378–1382 (2017)

    Article  Google Scholar 

  9. https://rslab.ut.ac.ir/data Accessed 25 May 2019

  10. Zhou, Z., Edoardo, P., Melba, M.C., James, C.T.: An active learning framework for hyperspectral image classification using hierarchical segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(2), 640–654 (2016)

    Article  Google Scholar 

  11. Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)

    Article  Google Scholar 

  12. Shalaby, M.M., Salem, M.A.-M., Khamis, A., Melgani, F.: Geometric model for vision-based door detection. In: 2014 9th International Conference on Computer Engineering & Systems (ICCES), Cairo, pp. 41–46 (2014). https://doi.org/10.1109/icces.2014.7030925

  13. Salem, M.A.-M., Appel, M., Winkler, F., Meffert, B.: FPGA-based smart camera for 3D wavelet-based image segmentation. In: 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC, pp. 1–8 (2008). https://doi.org/10.1109/icdsc.2008.4635720

  14. Salem, M.A.-M., Alaa, A., Alaa, S., Marwa, S.: Recent survey on medical image segmentation. In: Computer Vision: Concepts, Methodologies, Tools, and Applications, pp. 129–169. IGI Global (2018). https://doi.org/10.4018/978-1-5225-5204-8.ch006

  15. Jolliffe, I.: Principal Component Analysis. Springer, New York (2002)

    MATH  Google Scholar 

  16. Zhao, B., Zhong, Y., Zhang, L.: A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery. ISPRS J. Photogramm. Remote Sens. 116, 73–85 (2016)

    Article  Google Scholar 

  17. Olshausen, B.A., Field, D.J.: Sparse coding with an over complete basis set: a strategy employed by V1? Vis. Res. 37(23), 3311–3325 (1997)

    Article  Google Scholar 

  18. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  19. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

  20. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of International Conference on Learning Representations, pp. 1–13 (2015)

    Google Scholar 

  21. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  22. Salakhutdinov, R., Hinton, G.: An efficient learning procedure for deep Boltzmann machines. Neural Comput. 24(8), 1967–2006 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  23. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings Conference on Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

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

    Article  Google Scholar 

  25. Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L.: Land Use Classification in Remote Sensing Images by Convolutional Neural Networks. Cornell University, Ithaca (2015)

    Google Scholar 

  26. Zhong, Y., Fei, F., Zhang, L.: Large patch convolutional neural networks for the scene classification of high spatial resolution imagery. Appl. Remote Sens. 10(2), 025006 (2016)

    Article  Google Scholar 

  27. Marmanis, D., Datcu, M., Esch, T., Stilla, U.: Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geosci. Remote Sens. Lett. 13(1), 105–109 (2015)

    Article  Google Scholar 

  28. Shafaey, M.A., Salem, M.A.-M., Ebied, H.M., Al-Berry, M., Tolba, M.F.: Deep learning for satellite image classification. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, vol. 4, pp. 383–391 (2019)

    Google Scholar 

  29. Baofeng, G., Robert, I.D., Steve, R.G., James, D.B.: Improving hyperspectral band selection by constructing an estimated reference map. Appl. Remote Sens. 8, 083692 (2014)

    Article  Google Scholar 

  30. Hu, F., Xia, G., Wang, Z., Huang, X., Zhang, L., Sun, H.: Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2015–2030 (2015)

    Article  Google Scholar 

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Correspondence to Mayar A. Shafaey .

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Shafaey, M.A., Salem, M.AM., Al-Berry, M.N., Ebied, H.M., El-Dahshan, E.A., Tolba, M.F. (2020). Hyperspectral Image Classification Using Deep Learning Technique. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_31

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