An Approach of Transferring Pre-trained Deep Convolutional Neural Networks for Aerial Scene Classification

  • Nilakshi DeviEmail author
  • Bhogeswar Borah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)


Feature selection or feature extraction plays a vital role in image classification task. Since the advent of deep learning methods, significant efforts have been given by researchers to obtain an optimal feature set of images for improving classification performance. Though several deep architectures of Convolutional Neural Networks (CNNs) have been successfully designed but training such deep architectures with small datasets like aerial scenes often leads to overfitting hence affects the classification accuracy. To tackle this issue in past few works, pre-trained CNNs are adopted as feature extractor where features are directly transferred to train only the classification layer for classifying images on the target dataset. In this work, an approach of feature extraction is proposed where both “multi-layer” and “multi-model” features are extracted from pre-trained CNNs. “Multi-layer” features are concatenation of features from multiple layers within a same CNN and “Multi-model” are concatenation of features from different CNN models. The concatenated features are further reduced with some method to obtain an optimal feature set.


Convolutional neural network Feature extraction Transfer learning 


  1. 1.
    Anwer, R.M., Khan, F.S., van de Weijer, J., Molinier, M., Laaksonen, J.: Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. ISPRS J. Photogramm. Remote Sens. 138, 74–85 (2018)CrossRefGoogle Scholar
  2. 2.
    Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L.: Land use classification in remote sensing images by convolutional neural networks. arXiv preprint arXiv:1508.00092 (2015)
  3. 3.
    Hu, F., Xia, G.S., Hu, J., Zhang, L.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11), 14680–14707 (2015)CrossRefGoogle Scholar
  4. 4.
    Li, E., Xia, J., Du, P., Lin, C., Samat, A.: Integrating multilayer features of convolutional neural networks for remote sensing scene classification. IEEE Trans. Geosci. Remote Sens. 55(10), 5653–5665 (2017)CrossRefGoogle Scholar
  5. 5.
    Liu, Q., Hang, R., Song, H., Zhu, F., Plaza, J., Plaza, A.: Adaptive deep pyramid matching for remote sensing scene classification. arXiv preprint arXiv:1611.03589 (2016)
  6. 6.
    Sheng, G., Yang, W., Xu, T., Sun, H.: High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int. J. Remote Sens. 33(8), 2395–2412 (2012)CrossRefGoogle Scholar
  7. 7.
    Wang, G., Fan, B., Xiang, S., Pan, C.: Aggregating rich hierarchical features for scene classification in remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(9), 4104–4115 (2017)CrossRefGoogle Scholar
  8. 8.
    Wang, J., Luo, C., Huang, H., Zhao, H., Wang, S.: 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
  9. 9.
    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. ACM (2010)Google Scholar
  10. 10.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)Google Scholar
  11. 11.
    Zeng, D., Chen, S., Chen, B., Li, S.: Improving remote sensing scene classification by integrating global-context and local-object features. Remote Sens. 10(5), 734 (2018)CrossRefGoogle Scholar
  12. 12.
    Zhang, W., Tang, P., Zhao, L.: Remote sensing image scene classification using CNN-CapsNet. Remote Sens. 11(5), 494 (2019)CrossRefGoogle Scholar
  13. 13.
    Zou, Q., Ni, L., Zhang, T., Wang, Q.: Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12(11), 2321–2325 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of CSETezpur UniversityTezpurIndia

Personalised recommendations