Skip to main content
SpringerLink
Log in

Deep Learning and Machine Learning for Object Detection in Remote Sensing Images

  • Conference paper
  • First Online:
Signal and Information Processing, Networking and Computers (ICSINC 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 473))

Abstract

Object detection is one of the most effective ways to analyze the remote sensing (RS) images. In this paper, we focus on the prevalent object detection framework based on deep learning technology for RS images which contains three different stages, namely the region proposals generation, feature extraction, and classification. The review provides a clear picture of the challenges and possible development trends in this field. Typical methods under this framework are extensively reviewed and analyzed. Comparisons among traditional methods with deep learning methods are presented, in which supervised and unsupervised methods for RS scene target detection are deeply discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Qi, S., Ma, J., Lin, J., Li, Y., Tian, J.: Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images. IEEE Geosc. Remote Sens. Lett. 12(7), 1451–1455 (2015)

    Article  Google Scholar 

  2. Zhao, A., Fu, K., Sun, H., Sun, X., Li, F., Zhang, D., Wang, H.: An effective method based on ACF for aircraft detection in remote sensing images. IEEE Geosc. Remote Sens. Lett. 14(5), 744–748 (2017)

    Article  Google Scholar 

  3. Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_54

    Google Scholar 

  4. Hosang, J., Benenson, R., Dollár, P., Schiele, B.: What Makes for Effective Detection Proposals? IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 814–830 (2016)

    Article  Google Scholar 

  5. Chen, X., Xiang, S., Liu, C., Pan, C.H.: Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 11(10), 1797–1801 (2014)

    Article  Google Scholar 

  6. Zhang, F., Du, B., Zhang, L., Xu, M.: Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Trans. Geosc. Remote Sens. 54(9), 5553–5563 (2016)

    Article  Google Scholar 

  7. Zou, Z., Shi, Z.: Ship detection in spaceborne optical image with SVD networks. IEEE Trans. Geosci. Remote Sens. 54(10), 5832–5845 (2016)

    Article  Google Scholar 

  8. Xia, Y., Wan, S., Yue, L.:A novel algorithm for ship detection based on dynamic fusion model of multi-feature and support vector machine. In: 2011 Sixth International Conference on Image and Graphics (ICIG), pp. 521–526, August 2011

    Google Scholar 

  9. Bi, F., Zhu, B., Gao, L., Bian, M.: A visual search inspired computational model for ship detection in optical satellite images. IEEE Geosc. Remote Sens. Lett. 9(4), 749–753 (2012)

    Article  Google Scholar 

  10. Zhu, C., Zhou, H., Wang, R., Guo, J.: A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features. IEEE Trans. Geosci. Remote Sens. 48(9), 3446–3456 (2010)

    Article  Google Scholar 

  11. Bi, F., Liu, F., Gao, L.: A hierarchical salient-region based algorithm for ship detection in remote sensing images. In: Zeng, Z., Wang, J. (eds.) Advances in Neural Network Research and Applications. LNEE, vol. 67. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12990-2_85

    Chapter  Google Scholar 

  12. Xu, J., Sun, X., Zhang, D., Fu, K.: Automatic detection of inshore ships in high-resolution remote sensing images using robust invariant generalized hough transform. IEEE Geosci. Remote Sens. Lett. 11(12), 2070–2074 (2014)

    Article  Google Scholar 

  13. Tang, J., Deng, C., Huang, G., Zhao, B.: Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans. Geosci. Remote Sens. 53(3), 1174–1185 (2015)

    Article  Google Scholar 

  14. Shi, Z., Yu, X., Jiang, Z., Li, B.: Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature. IEEE Trans. Geosci. Remote Sens. 52(8), 4511–4523 (2014)

    Article  Google Scholar 

  15. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893, June (2005)

    Google Scholar 

  16. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, p. I (2001)

    Google Scholar 

  17. Lowe, D.: Distinctive Image Features from Scale-invariant Keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  18. Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang, J., Song, J., Chen, M., Yang, Z.: Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine. Int. J. Remote Sens. 36(12), 3144–3169 (2015)

    Article  Google Scholar 

  20. Jin, X., Davis, C.: Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks. Image Vis. Comput. 25(9), 1422–1431 (2007)

    Article  Google Scholar 

  21. Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(12), 7405–7415 (2016)

    Article  Google Scholar 

  22. Diao, W., Sun, X., Zheng, X., Dou, F., Wang, H., Fu, K.: Efficient saliency-based object detection in remote sensing images using deep belief networks. IEEE Geosci. Remote Sens. Lett. 13(2), 137–141 (2016)

    Article  Google Scholar 

  23. Zhou, P., Cheng, G., Liu, Z., Bu, S., Hu, X.: Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping. Multidimension. Syst. Signal Process. 27(4), 925–944 (2016)

    Article  MathSciNet  Google Scholar 

  24. Chen, X., Xiang, S., Liu, C., Pan, C.H.: Aircraft detection by deep belief nets. In: 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 54–58, November (2013)

    Google Scholar 

  25. Yokoya, N., Iwasaki, A.: Object detection based on sparse representation and hough voting for optical remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 8(5), 2053–2062 (2015)

    Article  Google Scholar 

  26. Chen, Z., Wang, C., Wen, C., Teng, X., Chen, Y., Guan, H., Li, J.: Vehicle detection in high-resolution aerial images via sparse representation and superpixels. IEEE Trans. Geosci. Remote Sens. 54(1), 103–116 (2016)

    Article  Google Scholar 

  27. Zhang, Y., Du, B., Zhang, L.: A sparse representation-based binary hypothesis model for target detection in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 53(3), 1346–1354 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing Institute of Spacecraft System Engineering, Beijing, 100094, China

    Guowei Yang & Qiang Luo

  2. Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing, 100081, China

    Yinding Yang & Yin Zhuang

Authors
  1. Guowei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yinding Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yin Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guowei Yang .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Songlin Sun

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Na Chen

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Tao Tian

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, G., Luo, Q., Yang, Y., Zhuang, Y. (2018). Deep Learning and Machine Learning for Object Detection in Remote Sensing Images. In: Sun, S., Chen, N., Tian, T. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2017. Lecture Notes in Electrical Engineering, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-10-7521-6_30

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7521-6_30

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7520-9

  • Online ISBN: 978-981-10-7521-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation