Skip to main content
SpringerLink
Log in

A Self-Supervised Learning Method for Shadow Detection in Remote Sensing Imagery

  • 3DR Express
  • Published:
3D Research

Abstract

Recent research progress in shadow detection has leveraged the development of remote sensing and computer vision. Since shadows of buildings, trees, bridges in one image can provide useful information about the scene to help people understand the shape, feature or estimate their locations and orientations of original objects, especially for damaged objects. In this study, a novel shadow detection algorithm for remote sensing imagery, called self-supervised learning method is proposed. The aim of this work is to generate shadow ratio threshold automatically without human interaction. To alleviate the traditional drawbacks of shadow detection, we fully combine supervised and unsupervised shadow detection method to suggest a self-supervised learning method, which supports us a strongly clue with establishing the relation of shadow and its original object. Subsequently, we benefit from gray-scale histogram to extract shadow segments, then shadow outlines are obtained. Finally, we assess the shadow detection performance of the proposed approach by comparing our results with the state-of-the-art methods. The results reveal the applicability and precision of the proposed self-supervised learning shadow detection technique.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Hu, F., Xia, G. S., Hu, J., et al. (2015). Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11), 14680–14707.

    Article  Google Scholar 

  2. Wu, H., Cheng, Z., Shi, W., et al. (2014). An object-based image analysis for building seismic vulnerability assessment using high-resolution remote sensing imagery. Natural Hazards, 71(1), 151–174.

    Article  Google Scholar 

  3. Celie, B. M., Boone, J., Dumortier, J., et al., (2016). Possible influences on the interpretation of functional domain (FD) near-infrared spectroscopy (NIRS): An explorative study. Applied Spectroscopy, 70(2), 363.

    Article  Google Scholar 

  4. Gao, J., Li, J., & Li, Y. (2015). Approximate event detection over multi-modal sensing data. Journal of Combinatorial Optimization, 32(4), 1–15.

    MathSciNet  Google Scholar 

  5. Gao, J., Li, J., Cai, Z., et al. (2015). Composite event coverage in wireless sensor networks with heterogeneous sensors. In Computer communications (pp. 217–225). IEEE.

  6. Li, P., Chen, Z., Yang, L. T., et al. (2017). Deep convolutional computation model for feature learning on big data in internet of things. IEEE Transactions on Industrial Informatics, 14(2), 790–798.

    Article  Google Scholar 

  7. Huang, G., Song, S., Gupta, J. N. D., et al. (2017). Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 44(12), 2405–2417.

    Article  Google Scholar 

  8. Thiagarajan, J. J., Ramamurthy, K. N., & Spanias, A. (2014). Multiple kernel sparse representations for supervised and unsupervised learning. IEEE Transactions on Image Processing, 23(7), 2905–2915.

    Article  MathSciNet  MATH  Google Scholar 

  9. Volkovs, M. N., & Zemel, R. S. (2014). New learning methods for supervised and unsupervised preference aggregation. The Journal of Machine Learning Research, 15(1), 1135–1176.

    MathSciNet  MATH  Google Scholar 

  10. Le, H., Vicente, T. F. Y., Nguyen, V., et al. (2018). A+D net: Training a shadow detector with adversarial shadow attenuation. http://cn.arxiv.org/pdf/1712.01361.

  11. Tian, J., Qi, X., Qu, L., et al. (2016). New spectrum ratio properties and features for shadow detection. Pattern Recognition, 51(C):, 85–96.

    Article  Google Scholar 

  12. Zhang, Q., Yang, L. T., Chen, Z., et al. (2017). PPHOPCM: Privacy-preserving high-order possibilistic c-Means algorithm for big data clustering with cloud computing. IEEE Transactions on Big Data. https://doi.org/10.1109/TBDATA.2017.2701816.

    Article  Google Scholar 

  13. Zhang, Q., Yang, L. T., Chen, Z., et al. (2018). A survey on deep learning for big data. Information Fusion, 42, 146–157.

    Article  Google Scholar 

  14. Jia, K., Li, Q., Wei, X., et al. (2015). Multi-temporal remote sensing data applied in automatic land cover update using iterative training sample selection and Markov Random Field model. Geocarto International, 30(8), 1–12.

    Article  Google Scholar 

  15. Liu, Y., & Li, X. (2014). Domain adaptation for land use classification: A spatio-temporal knowledge reusing method. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 133–144.

    Article  Google Scholar 

  16. Tahmoresnezhad, J., & Hashemi, S. (2016). Visual domain adaptation via transfer feature learning. Knowledge and Information Systems, 50, 1–21.

    Google Scholar 

  17. Zhang, L., Zuo, W., & Zhang, D. (2016). LSDT: Latent sparse domain transfer learning for visual adaptation. IEEE Transactions on Image Processing, 25(3), 1177–1191.

    Article  MathSciNet  Google Scholar 

  18. Zhang, L., & Zhang, D. (2016). Robust visual knowledge transfer via extreme learning machine based domain adaptation. IEEE Transactions on Image Processing, 25(10), 4959–4973.

    Article  MathSciNet  Google Scholar 

  19. Long, M., Wang, J., Ding, G., et al. (2014). Transfer joint matching for unsupervised domain adaptation. In Computer vision and pattern recognition (pp. 1410–1417). IEEE.

  20. Cote, M., & Saeedi, P. (2013). Automatic rooftop extraction in nadir aerial imagery of suburban regions using corners and variational level set evolution. IEEE Transactions on Geoscience and Remote Sensing, 51(1), 313–328.

    Article  Google Scholar 

  21. Ok, A. O., Senaras, C., & Yuksel, B. (2013). Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 51(3), 1701–1717.

    Article  Google Scholar 

  22. Xia, H., Song, S., & He, L. (2016). A modified Gaussian mixture background model via spatiotemporal distribution with shadow detection. Signal, Image and Video Processing, 10(2), 343–350.

    Article  Google Scholar 

  23. Movia, A., Beinat, A., & Crosilla, F. (2016). Shadow detection and removal in RGB VHR images for land use unsupervised classification. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 485–495.

    Article  Google Scholar 

  24. Ghimire, D., & Lee, J. (2016). Online sequential extreme learning machine-based co-training for dynamic moving cast shadow detection. Multimedia Tools and Applications, 75(18), 11181–11197.

    Article  Google Scholar 

  25. Wang, B., Zhu, W., Zhao, Y., et al. (2015). Moving cast shadow detection using joint color and texture features with neighboring information. Revised selected papers of the Psivt 2015 workshops on image and video technology (pp. 15–25). Springer, New York.

    Chapter  Google Scholar 

  26. Martelbrisson, N., & Zaccarin, A. (2005). Moving cast shadow detection from a Gaussian mixture shadow model (Vol. 2, pp. 643–648).

  27. Tian, Y. M., & Wang, X. T. (2010). A fast convergent Gaussian mixture model in moving object detection with shadow elimination. In International conference on E-Product E-Service and E-Entertainment (pp. 1–4). IEEE.

  28. Amato, A., Huerta, I., Mozerov, M. G., et al. (2014). Moving cast shadows detection methods for video surveillance applications. In V. K. Asari (Ed.), Wide area surveillance. Augmented vision and reality (Vol. 6, pp. 23–47). Berlin: Springer.

    Chapter  Google Scholar 

  29. Martel-Brisson, N., & Zaccarin, A. (2007). Learning and removing cast shadows through a multidistribution approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(7), 1133–46.

    Article  Google Scholar 

  30. Nia, M. S., Wang, Z. D., Gader, P. D., et al. (2015). Impact of atmospheric correction and image filtering on hyperspectral classification of tree species using support vector machine. Journal of Applied Remote Sensing, 9(1), 095990.

    Article  Google Scholar 

  31. Sabnis, M. K., & Shukla, M. K. (2016). Model-based approach for shadow detection of static images. In A. Chakrabarti, N. Sharma, & V. E. Balas (Eds.), Advances in computing applications. Singapore: Springer.

    Google Scholar 

  32. Wang, Q., Yan, L., Yuan, Q., et al. (2017). An automatic shadow detection method for VHR remote sensing orthoimagery. Remote Sensing, 9(5), 469.

    Article  Google Scholar 

  33. Sun, J., Tian, J., Du, Y., et al. (2009). Retinex theory-based shadow detection and removal in single outdoor image. Industrial Robot, 36(3), 263–269.

    Article  Google Scholar 

  34. Finlayson, G. D., Hordley, S. D., & Drew, M. S. (2002). Removing shadows from images. In Computer vision ECCV (pp. 823–836).

    Chapter  Google Scholar 

  35. Makarau, A., Richter, R., Muller, R., et al. (2011). Adaptive shadow detection using a blackbody radiator model. IEEE Transactions on Geoscience and Remote Sensing, 49(6), 2049–2059.

    Article  Google Scholar 

  36. Jung, C., Kim, W., & Kim, C. (2011). Detecting shadows from a single image. Optics Letters, 36(22), 4428.

    Article  Google Scholar 

  37. Khan, S., Bennamoun, M., Sohel, F., et al. (2016). Automatic shadow detection and removal from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 431–446.

    Article  Google Scholar 

  38. Safari, L., Amaro, P., Fritzche, S., et al. (2012). Relativistic total cross section and angular distribution for Rayleigh scattering by atomic hydrogen. Physics, 85(4), 1354–1362.

    Google Scholar 

  39. Finlayson, G. D., & Hordley, S. D. (2001). Color constancy at a pixel. Journal of the Optical Society of America a Optics Image Science and Vision, 18(2), 253.

    Article  Google Scholar 

  40. Onyango, C. M., & Marchant, J. A. (2002). Spectral invariance under daylight illumination changes. Journal of the Optical Society of America a Optics Image Science and Vision, 19(5), 840.

    Article  Google Scholar 

  41. Besbes, O., & Benazza-Benyahia, A. (2016). A novel video-based smoke detection method based on color invariants. In IEEE international conference on acoustics, speech and signal processing (pp. 1911–1915). IEEE.

  42. Kviatkovsky, I., Adam, A., & Rivlin, E. (2013). Color invariants for person reidentification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1622–34.

    Article  Google Scholar 

  43. Teke, M., Baeski, E., Ok, A., et al. (2011). Multi-spectral false color shadow detection. In Photogrammetric image analysis (pp. 109–119). Berlin: Springer.

    Chapter  Google Scholar 

  44. Sirmacek, B., & Unsalan, C. (2009). Damaged building detection in aerial images using shadow information. In International conference on recent advances in space technologies (pp. 249–252). IEEE.

  45. Ghimire, D., & Lee, J. (2016). Online sequential extreme learning machine-based co-training for dynamic moving cast shadow detection. Multimedia Tools and Applications, 75(18), 11181–11197.

    Article  Google Scholar 

  46. Senaras, C., & Vural, F. T. Y. (2016). A self-supervised decision fusion framework for building detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), 1780–1791.

    Article  Google Scholar 

  47. Khan, S. H., Bennamoun, M., & Sohel, F. (2016). Automatic shadow detection and removal from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 431–446.

    Article  Google Scholar 

  48. Blakey, T., Melesse, A., & Hall, M. (2015). Supervised classification of benthic reflectance in shallow subtropical waters using a generalized pixel-based classifier across a time series. Remote Sensing, 7(5), 5098–5116.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Software College, Shenyang Normal University, Shenyang, China

    Shoulin Yin, Jie Liu & Hang Li

Authors
  1. Shoulin Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, S., Liu, J. & Li, H. A Self-Supervised Learning Method for Shadow Detection in Remote Sensing Imagery. 3D Res 9, 51 (2018). https://doi.org/10.1007/s13319-018-0204-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13319-018-0204-9

Keywords

Search

Navigation