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Aerial Scene Classification with Convolutional Neural Networks

  • Sibo Jia
  • Huaping Liu
  • Fuchun Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

A robust satellite image classification is the fundamental step for aerial image understanding. However current methods with hand-crafted features and conventional classifiers have limited performance. In this paper we introduced convolutional neural network (CNN) method into this problem. Two approaches, including using conventional classifier with CNN features and direct classification with trained CNN models, are investigated with experiments. Our method achieved 97.4% accuracy on 5-fold cross-validation test of the UCMERCED LULC dataset, which is 8% higher than state-of-the-art methods.

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References

  1. 1.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp. 2278–2324 (1998)Google Scholar
  2. 2.
    Krizhevsky, A., Ilya, S., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)Google Scholar
  3. 3.
    Hannun, A.Y., Case, C., Casper, J., Catanzaro, B.C., Diamos, G., Elsen, E., Prenger, R., Satheesh, S., Sengupta, S., Coates, A., Ng, A.Y.: Deep speech: Scaling up end-to-end speech recognition. In: arXiv:1412.5567Google Scholar
  4. 4.
    Bottou, L.: Stochastic gradient descent tricks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn., LNCS, vol. 7700, pp. 421–436. Springer, Heidelberg (2012)Google Scholar
  5. 5.
    Cheriyadat, A.M.: Unsupervised feature learning for aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 439–451 (2014)Google Scholar
  6. 6.
    Chen, S., Tian, Y.: Pyramid of Spatial Relatons for Scene-Level Land Use Classification. IEEE Transactions on Geoscience and Remote Sensing, 1947–1957 (2015)Google Scholar
  7. 7.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)Google Scholar
  8. 8.
    Yang, Y., Newsam, S.: 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, pp. 270–279 (2010)Google Scholar
  9. 9.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. In: arXiv:1409.0575 (2014)Google Scholar
  10. 10.
    Yangqing, J., Evan, S., Jeff, D., Sergey, K., Jonathan, L., Ross, G., Sergio, G., Trevor, D.: Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675–678 (2014)Google Scholar
  11. 11.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: arXiv:1409.4842Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

<SimplePara><Emphasis Type="Bold">Open Access</Emphasis> This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. </SimplePara> <SimplePara>The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</SimplePara>

Authors and Affiliations

  • Sibo Jia
    • 1
    • 2
  • Huaping Liu
    • 1
    • 2
  • Fuchun Sun
    • 1
    • 2
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.State Key Lab. of Intelligent Technology and SystemsBeijingChina

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