Remote Sensing Classification Under Deep Learning: A Review

  • Manoj Kumar SharmaEmail author
  • Harshit Verma
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 141)


The best machine learning strategy is deep neural learning. Regardless of information wellsprings, current classification and remote sensing strategies are quickly presented. At that point, basic databases and run deep neural learning models are introduced, with deep belief network, convolutional neural network and stacked autoencoder. Besides, ideal design of such strategies for deep neural learning is abridged by Kappa coefficient and general exactness At long last, the present work along with future scope of satellite sensing deep neural learning classification has been provided. The present work explores the deep neural learning, which is guaranteed to be an overwhelming strategy for sensing classification of remote sensing.


Sensing classification Remote sensing CNN Deep neural learning Kappa coefficient 


  1. 1.
    Alpaydin, E.: Introduction to Machine Learning, 3rd edn, pp. 267–311. MIT, London (2014)zbMATHGoogle Scholar
  2. 2.
    Sharma, M.K., et al.: Offline scripting-free author identification based on speeded-up robust features. Int. J. Doc. Anal. Recognit. 4(18), 303–316 (2015)Google Scholar
  3. 3.
    Sharma, M.K., et al.: Offline language-free writer identification based on speeded-up robust features. Int. J. Eng. (IJE) 28(7), 984–994 (2015)Google Scholar
  4. 4.
    Sharma, M.K., et al.: A pixel plot and traced based segmentation of english offline handwritten cursive scripts using a feedforward neural network. Int. J. Neural Comput. Appl. 7(5), 369–1379 (2015)Google Scholar
  5. 5.
    Sharma, M.K., et al.: An efficient segmentation technique for devanagari offline handwritten scripts using the feedforward neural network. Int. J. Neural Comput. Appl. 26(8), 881–1893 (2015)Google Scholar
  6. 6.
    Sharma, M.K., et al.: Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network. Int. J. Neural Comput. Appl. 27(7), 1817–1829 (2016)Google Scholar
  7. 7.
    Peng, W.L., et al.: Introduction to Remote Sensing, pp. 192–202. Higher Education Press, Beijing (2006)Google Scholar
  8. 8.
    Salakhutdinov, R.R.: Reduce the dimensionality data with neural network. Science 313, 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Yu, B., et al.: A key of step in the era of big data. J. Eng. Stud. 6(3), 233–243 (2014)MathSciNetGoogle Scholar
  10. 10.
    Yoshua, B., et al.: A deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  11. 11.
    Cheng, X., et al.: Progress report on deep learning. CAAI Trans. Intell. Syst. 11(5), 567–577 (2016)Google Scholar
  12. 12.
    Bengio, Y.: Learn deep architectures for artificial intelligence. Found. Trends Mach. Learn. 2(1), 101–127 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Han, X.Y., Han, L., Liu, D.W.: High spatial resolution remote sensing classification based on deep learning. Acta Opt. Sin. 36(4), 0428001-1–0428001-9 (2016)Google Scholar
  14. 14.
    Yuan, H., Ou Yang, N., Gao, X.: A hyper spectral sensing classification method based on fast denoising and DBN. J. Guilin Univ. Electron. Technol. 6(6), 469–476 (2016)Google Scholar
  15. 15.
    Zhao, X., et al.: Spectral-spatial classification of hyperspectral data based on DBN. Appl. Earth Obs. Remote Sens. 8(6), 2381–2392 (2015)Google Scholar
  16. 16.
    Dou, Y., et al.: Remote sensing image classification based on DBN model. J. Comput. Res. Dev. 51(9), 1911–1918 (2014)Google Scholar
  17. 17.
    Zhang, J.P., et al.: Classification of hyperspectral sensing based on DBN. ICIP, pp. 5132–5136 (2015)Google Scholar
  18. 18.
    Huang, X.Q., Yu, X.G.: Deep neural networks based on hyper spectral sensing classification. Electr. Meas. Tech. 29(7), 81–86 (2016)Google Scholar
  19. 19.
    Yu, Q., Liang, H.M.: Hyper spectral sensing classification using sparse repress of convolutional neural network features. Remote Sens. 8(2), 99–105 (2016)Google Scholar
  20. 20.
    Verdoliva, L., Sansone, C., Poggi, G., Castelluccio, M.: Land use classification in remote sensing by convolutional neural networks. Acta Ecol. Sin. 28(2), 627–635 (2015)Google Scholar
  21. 21.
    Gao, X., Sun, X., Qu, J.Y.: Remote sensing target recognition. Foreign Elect. Measu. Tech. 8, 45–50 (2016)Google Scholar
  22. 22.
    Chen, et al.: Remote_sensing detection of rural-buildings based on DCN learning algorithm. Sci. Surv. Mapp. 39(5), 227–230 (2016)Google Scholar
  23. 23.
    Yu, H.C., Zhang, F., Wei, L., Huang, Y.Y., Hu, W.: Deep convolutional neural network for hyper spectral sensing classification. J. Sens. 2, 1–12 (2015)Google Scholar
  24. 24.
    Gu, H.Y.,. Yu, F., Yu, H.T., Cao, L.L., Han,: Application of convolutional neural networks in classification of high resolution remote sensingry, Sci. Surv. Mapp. 41(9), 170–175 (2016)Google Scholar
  25. 25.
    Lin., Hyper spectral sensing feature extraction and classification based on auto encoders (2014)Google Scholar
  26. 26.
    Yu, M., Yang X.Q., Chen, X.: Stacked denoise autoencoder based feature extraction and classification for hyperspectral sensing. J. Sens. 1–10 (2016)Google Scholar
  27. 27.
    Clinton, N., Yu, W.J., et al.: Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping. Int. J. Remote Sens. 37(23), 5632–5646 (2016)Google Scholar
  28. 28.
    Tian, W., Wang, J.G., Cao, T., Zhang, Y.H., Kan, X.: Snow cover recognition for Qinghai-Tibet and acta Sinica, 45(10), 1210–1221 (2016)Google Scholar
  29. 29.
    Ouyang, C., Zhang, F., Chen, Z., Zhang, Y.F.: Remote sensing image classification based on stacked denoising autoencoder. J. Comput. Appl. 36(S2), 171–174 (2016)Google Scholar
  30. 30.
    Qi. B., Wan, X.Q., Zhao, C.H., et al.: Spectral-spatial classification of hyperspectral imagery based on stacked sparse autoencoder and random forest. Eur. J. Remote Sens. 50(1), 47–63 (2017)Google Scholar
  31. 31.
    Dou, Y., Lv, Q., et al.: Urban land use and land cover classification using remotely sensed SAR data through DBNs. J. Sens. 1–10 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer and Communication Engineering, School of Computing & ITManipal University JaipurJaipurIndia

Personalised recommendations