Multiscale Satellite Image Classification Using Deep Learning Approach

  • Noureldin LabanEmail author
  • Bassam Abdellatif
  • Hala M. Ebied
  • Howida A. Shedeed
  • Mohamed F. Tolba
Part of the Studies in Computational Intelligence book series (SCI, volume 836)


Image classification has been acquiring special importance in the practical applications of remote sensing. This is done with the extraordinary rise of spatial and spectral resolution of satellite imaging sensors. Also it comes from the daily increase of remote sensing databases. Deep learning approaches, especially Convolutional Neural Networks (CNNs) techniques, have been recently outperforming other state-of-the-art classification approaches in various domains. In this chapter, we propose an enhanced technique for classification of satellite images using CNNs. There are two characteristics of satellite images that make performance issue very crucial; first, high information content within the satellite image, and secondly, high computational requisites involved by CNNs. The improvement technique is built on an effective selection of suitable image scale. As this scale achieves a respectively high classification accuracy alongside a minimal computational use. We conduct our proposed technique using three state-of-the-art datasets: WHU-RS Dataset, UCMerced Land Use Dataset, and Brazilian Coffee Scenes Dataset. The proposed technique results in enhancing the accuracy performance, instead of using the original scale directly.


  1. 1.
    M. Das, S.K. Ghosh, Deep-STEP: a deep learning approach for spatiotemporal prediction of remote sensing data. IEEE Geosci. Remote Sens. Lett. 13(12), 1984–1988 (2016)CrossRefGoogle Scholar
  2. 2.
    D.M.M. Hordiiuk, V.V.V Hnatushenko, Neural network and local laplace filter methods applied to very high resolution remote sensing imagery in urban damage detection, in 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF) (2017), pp. 363–366Google Scholar
  3. 3.
    D. Marmanis, M. Datcu, T. Esch, U. Stilla, S. Member, Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geosci. Remote Sens. Lett. 13(1), 1–5 (2015)Google Scholar
  4. 4.
    Y. Yang, S. Newsam, Bag-of-visual-words and spatial extensions for land-use classification, in Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (2010), pp. 270–279Google Scholar
  5. 5.
    A. Romero, C. Gatta, G. Camps-valls, S. Member, Unsupervised deep feature extraction for remote sensing image classification. IEEE Geosci. Remote Sens. Lett. 54(3), 1–14 (2015)Google Scholar
  6. 6.
    G. Cheng, J. Han, A survey on object detection in optical remote sensing images. ISPRS J. Photogramm. Remote Sens. 117, 11–28 (2016)CrossRefGoogle Scholar
  7. 7.
    Z. Liu, B. Tang, X. He, Q. Qiu, F. Liu, Class-specific random forest with cross-correlation constraints for spectral—spatial hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 14(2), 257–261 (2017)CrossRefGoogle Scholar
  8. 8.
    Z. Wu, W. Lin, Z. Zhang, A. Wen, L. Lin, An ensemble random forest algorithm for insurance big data analysis, in 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), vol. 5 (2017), pp. 531–536Google Scholar
  9. 9.
    T. L. M. Barreto et al. Classification of detected changes from multitemporal high-res Xband SAR images: intensity and texture descriptors from SuperPixels. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(12), 5436–5448 (2016)CrossRefGoogle Scholar
  10. 10.
    B. Zheng, S.W. Myint, P.S. Thenkabail, R.M. Aggarwal, A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. Int. J. Appl. Earth Obs. Geoinformation 34(1), 103–112 (2015)CrossRefGoogle Scholar
  11. 11.
    L. Deng, D. Yu, Deep learning: methods and applications. Found Trends Signal Process. 7(3–4), pp. 197–387 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    G. Cheng, P. Zhou, J. Han, Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(99), 7405–7415 (2016)CrossRefGoogle Scholar
  13. 13.
    E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez, Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans. Geosci. Remote Sens. 55(2), 645–657 (2016)CrossRefGoogle Scholar
  14. 14.
    K. Nogueira, W.O. Miranda, J.A. Dos Santos, Improving spatial feature representation from aerial scenes by using convolutional networks, in Brazilian Symposium on Computer Graphics and Image Processing, vol. 2015, pp. 289–296 (2015)Google Scholar
  15. 15.
    A. Fernández, Á. Gómez, F. Lecumberry, Á. Pardo, I. Ramírez, Pattern recognition in Latin America in the ‘big data’ era. Pattern Recognit. 48(4), 1181–1192 (2015)CrossRefGoogle Scholar
  16. 16.
    L. Zhou, S. Pan, J. Wang, A.V. Vasilakos, Machine learning on Big Data: opportunities and challenges. Neurocomputing 237(January), 350–361 (2017)CrossRefGoogle Scholar
  17. 17.
    G.-S. Xia et al., Structural high-resolution satellite image indexing, in ISPRS TC VII Symposium—100 Years ISPRS, vol. XXXVIII, pp. 298–303 (2010)Google Scholar
  18. 18.
    A.B. Penatti, K. Nogueira, J.A. Santos, O.A.B. Penatti, K. Nogueira, J.A. dos Santos, Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? in 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 44–51 (2015)Google Scholar
  19. 19.
    K. Nogueira, O.A.B.O.A.B. Penatti, J.A. Dos Santos, Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognit. 61, 539–556 (2016)CrossRefGoogle Scholar
  20. 20.
    H. Wu, B. Liu, W. Su, W. Zhang, J. Sun, Deep filter banks for land-use scene classification. IEEE Geosci. Remote Sens. Lett. 13(12), 1895–1899 (2016)CrossRefGoogle Scholar
  21. 21.
    M. Volpi, D. Tuia, Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55(2), 881–893 (2016)CrossRefGoogle Scholar
  22. 22.
    J. Wang, C. Luo, H. Huang, H. Zhao, S. Wang, Transferring pre-trained deep CNNs for remote scene classification with general features learned from linear PCA network. Remote Sens. 9(3), 225 (2017)CrossRefGoogle Scholar
  23. 23.
    M. Längkvist, A. Kiselev, M. Alirezaie, A. Loutfi, Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sens. 8(4), 329 (2016)CrossRefGoogle Scholar
  24. 24.
    A. Krizhevsky, I. Sutskever, G.E. Hinton, {ImageNet} classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1–9 (2012)Google Scholar
  25. 25.
    W. Diao, X. Sun, X. Zheng, F. Dou, H. Wang, K. Fu, Efficient saliency-based object detection in remote sensing images using deep belief networks. IEEE Geosci. Remote Sens. Lett. 13(2), 137–141 (2016)CrossRefGoogle Scholar
  26. 26.
    X. Yao, J. Han, S. Member, G. Cheng, X. Qian, L. Guo, Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Trans. Geosci. Remote Sens. 54(6), 1–12 (2016)CrossRefGoogle Scholar
  27. 27.
    C. Farabet, C. Couprie, L. Najman, Y. LeCun, Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)CrossRefGoogle Scholar
  28. 28.
    X.Z. Member, S. Li, F.T. Member, K. Qin, S. Hu, S. Liu, Deep learning with grouped features for spatial spectral classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 14(1), 1–5 (2017)CrossRefGoogle Scholar
  29. 29.
    D. Tuia, R. Flamary, N. Courty, Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions. ISPRS J. Photogramm. Remote Sens. 105, 272–285 (2015)CrossRefGoogle Scholar
  30. 30.
    Y. Zhou, H. Wang, S. Member, F. Xu, S. Member, Y. Jin, Polarimetric SAR image classification using deep convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 13(12), 1935–1939 (2016)CrossRefGoogle Scholar
  31. 31.
    Y. Yu, J. Li, S. Member, H. Guan, C. Wang, Automated detection of three-dimensional cars in mobile laser scanning point clouds using DBM-Hough-Forests. IEEE Trans. Geosci. Remote Sens. 54(7), 4130–4142 (2016)CrossRefGoogle Scholar
  32. 32.
    M.M. Najafabadi, F. Villanustre, T.M. Khoshgoftaar, N. Seliya, R. Wald, E. Muharemagic, Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015)CrossRefGoogle Scholar
  33. 33.
    E. Aptoula, M.C. Ozdemir, B. Yanikoglu, Deep learning with attribute profiles for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 13(12), 1970–1974 (2016)CrossRefGoogle Scholar
  34. 34.
    E. Basaeed, H. Bhaskar, M. Al-Mualla, Supervised remote sensing image segmentation using boosted convolutional neural networks. Knowl. Based Syst. 99, 19–27 (2016)CrossRefGoogle Scholar
  35. 35.
    M.D. Tissera, M.D. McDonnell, Deep extreme learning machines: supervised autoencoding architecture for classification. Neurocomputing 174, 42–49 (2016)CrossRefGoogle Scholar
  36. 36.
    X. Zhang, Y. Liang, Y. Zheng, J. An, L.C. Jiao, Hierarchical discriminative feature learning for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 13(4), 594–598 (2016)CrossRefGoogle Scholar
  37. 37.
    T.H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, Y. Ma, PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  38. 38.
    N. Kussul, M. Lavreniuk, S. Skakun, A. Shelestov, Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(5), 778–782 (2017)CrossRefGoogle Scholar
  39. 39.
    S. Mei, J. Ji, J. Hou, X. Li, Q. Du, Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55(8), 4520–4533 (2017)CrossRefGoogle Scholar
  40. 40.
    E. Ferreira, A. de A. Araujo, J. A. dos Santos, A boosting-based approach for remote sensing multimodal image classification, in 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 416–423 (2016)Google Scholar
  41. 41.
    I. H. Ikasari, V. Ayumi, M. I. Fanany, S. Mulyono, Multiple regularizations deep learning for paddy growth stages classification from LANDSAT-8, in 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 512–517 (2016)Google Scholar
  42. 42.
    Q. Lv, X. Niu, Y. Dou, J. Xu, Y. Lei, Classification of hyperspectral remote sensing image using hierarchical local-receptive-field-based extreme learning machine. IEEE Geosci. Remote Sens. Lett. 13(3), 434–438 (2016)Google Scholar
  43. 43.
    Z. Zhao, L. Jiao, J. Zhao, J. Gu, J. Zhao, Discriminant deep belief network for high-resolution SAR image classification. Pattern Recognit. 61, 686–701 (2016)CrossRefGoogle Scholar
  44. 44.
    T. Zhang, Q. Wang, Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12(11), 2321–2325 (2015)CrossRefGoogle Scholar
  45. 45.
    W. Li, G. Wu, F. Zhang, Q. Du, Hyperspectral image classification using deep pixel-pair features. IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017)CrossRefGoogle Scholar
  46. 46.
    C. Bentes, D. Velotto, B. Tings, Ship classification in TerraSAR-X images with convolutional neural networks. IEEE J. Ocean. Eng. 43(1), 258–266 (2018)CrossRefGoogle Scholar
  47. 47.
    W. Zhou, S. Newsam, C. Li, Z. Shao, Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval. Remote Sens. 9(5), 489 (2017)CrossRefGoogle Scholar
  48. 48.
    Q. Liu, R. Hang, H. Song, Z. Li, Learning multiscale deep features for high-resolution satellite image scene classification. IEEE Trans. Geosci. Remote Sens. 56(1), 117–126 (2018)CrossRefGoogle Scholar
  49. 49.
    S. Wang, D. Quan, X. Liang, M. Ning, Y. Guo, L. Jiao, A deep learning framework for remote sensing image registration. ISPRS J. Photogramm. Remote Sens. (2018)Google Scholar
  50. 50.
    H. Lu, X. Fu, C. Liu, L. Li, Y. He, N. Li, Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning. J. Mt. Sci. 14(4), 731–741 (2017)CrossRefGoogle Scholar
  51. 51.
    J. Sokolic, R. Giryes, G. Sapiro, M.R.D. Rodrigues, Robust large margin deep neural networks. IEEE Trans. Signal Process. 65(16), 4265–4280 (2017)MathSciNetzbMATHCrossRefGoogle Scholar
  52. 52.
    I. Goodfellow, Y. Bengio, A. Courville, Deep Learning. The MIT Press (2016)Google Scholar
  53. 53.
    M.B.A. Djamgoz, S. Vallerga, H.-J. Wagner, Functional organization of the outer retina in aquatic and terrestrial vertebrates: comparative aspects and possible significance to the ecology of vision, in Adaptive Mechanisms in the Ecology of Vision, ed. by S.N. Archer, M.B.A. Djamgoz, E.R. Loew, J.C. Partridge, S. Vallerga (Springer, Netherlands, Dordrecht, 1999), pp. 329–382CrossRefGoogle Scholar
  54. 54.
    Y. Chen, H. Jiang, C. Li, X. Jia, P. Ghamisi, Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016)CrossRefGoogle Scholar
  55. 55.
    M. Abadi, et al., TensorFlow: a system for large-scale machine learning, in Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, pp. 265–283 (2016)Google Scholar
  56. 56.
    P. Reverdy, N.E. Leonard, Parameter estimation in softmax decision-making models with linear objective functions. IEEE Trans. Autom. Sci. Eng. 13(1), 54–67 (2015)CrossRefGoogle Scholar
  57. 57.
    G. Cheng, J. Han, X. Lu, Remote sensing image scene classification: BENCHMARK and state of the art. 105(10), 1–19, arXiv:1703.00121 [cs.CV] (2001)
  58. 58.
    X. Bian, C. Chen, L. Tian, Q. Du, Fusing local and global features for high-resolution scene classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (99), 1–13 (2017)Google Scholar
  59. 59.
    Y. Zhou, J. Li, L. Feng, X. Zhang, X. Hu, Adaptive scale selection for multiscale segmentation of satellite images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (99), 1–11 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Noureldin Laban
    • 1
    Email author
  • Bassam Abdellatif
    • 1
  • Hala M. Ebied
    • 2
  • Howida A. Shedeed
    • 2
  • Mohamed F. Tolba
    • 2
  1. 1.Data Reception and Analysis DivisionNational Authority for Remote, Sensing and Space ScienceCairoEgypt
  2. 2.Faculty of Computer and Information SciencesAin Shams UniversityCairoEgypt

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