Skip to main content
SpringerLink
Log in

Cloud Detection Using Super Pixel Classification and Semantic Segmentation

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Cloud detection plays a very significant role in remote sensing image processing. This paper introduces a cloud detection method based on super pixel level classification and semantic segmentation. Firstly, remote sensing images are segmented into super pixels. Segmented super pixels compose a super pixel level remote sensing image database. Though cloud detection is essentially a binary classification task, our database is labeled into four categories to improve the generalization ability: thick cloud, cirrus cloud, building, and other culture. Secondly, the super pixel level database is used to train our cloud detection models based on convolution neural network (CNN) and deep forest. Hierarchical fusion CNN is proposed considering super pixel level images contain less semantic information than normal images. Taking full advantage of low-level features like color and texture information, it is more applicable for super pixel level classification. Besides, a distance metric is proposed to refine ambiguous super pixels. Thirdly, an end-to-end cloud detection model based on semantic segmentation is introduced. This model has no restrictions on the input size, and takes less time. Experimental results show that compared with other cloud detection methods, our proposed method achieves better performance.

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

Similar content being viewed by others

References

  1. Xie F, Shi M, Shi Z, Yin J, Zhao D. Multilevel cloud detection in remote sensing images based on deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3631-3640.

    Article  Google Scholar 

  2. Shi M, Xie F, Zi Y, Yin J. Cloud detection of remote sensing images by deep learning. In Proc. the 2016 IEEE International Geoscience and Remote Sensing Symposium, July 2016, pp.701-704.

  3. Liu H, Zeng D, Tian Q. Super pixel cloud detection using hierarchical fusion CNN. In Proc. the 4th IEEE International Conference on Multimedia Big Data, September 2018, Article No. 32.

  4. Zhou Z H, Feng J. Deep forest: Towards an alternative to deep neural networks. arXiv:1702.08835, 2017. https://arxiv.org/abs/1702.08835, May 2018.

  5. Jedlovec G J, Haines S L, LaFontaine F J. Spatial and temporal varying thresholds for cloud detection in GOES imagery. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6): 1705-1717.

    Article  Google Scholar 

  6. Zhang Q, Xiao C. Cloud detection of RGB color aerial photographs by progressive refinement scheme. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11): 7264-7275.

    Article  Google Scholar 

  7. Zhong B, Chen W, Wu S, Hu L, Luo X, Liu Q. A cloud detection method based on relationship between objects of cloud and cloud-shadow for Chinese moderate to high resolution satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(11): 4898-4908.

    Article  Google Scholar 

  8. Li Z, Shen H, Li H, Xia G, Gamba P, Zhang L. Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery. Remote Sensing of Environment, 2017, 191: 342-358.

    Article  Google Scholar 

  9. Visa A, Valkealahti K, Simula O. Cloud detection based on texture segmentation by neural network methods. In Proc. the 1991 IEEE International Joint Conference on Neural Networks, Vol. 2, November 1991, pp.1001-1006.

  10. Zhan Y, Wang J, Shi J, Cheng G, Yao L, Sun W. Distinguishing cloud and snow in satellite images via deep convolutional network. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1785-1789.

    Article  Google Scholar 

  11. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.

    Article  Google Scholar 

  12. Hu X, Wang Y, Shan J. Automatic recognition of cloud images by using visual saliency features. IEEE Geosci. Remote Sensing Lett., 2015, 12(8): 1760-1764.

    Article  Google Scholar 

  13. Tan K, Zhang Y, Tong X. Cloud extraction from Chinese high resolution satellite imagery by probabilistic latent semantic analysis and object-based machine learning. Remote Sensing, 2016, 8(11): Article No. 963.

  14. Yuan Y, Hu X. Bag-of-words and object-based classification for cloud extraction from satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 4197-4205.

    Article  Google Scholar 

  15. Li Z, Shen H, Wei Y, Cheng Q, Yuan Q. Cloud detection by fusing multi-scale convolutional features. In Proc. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Science, May 2018, pp.149-152.

  16. Ma C, Luo G, Wang K. Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Transactions on Medical Imaging, 2018, 37(8): 1943-1954.

    Article  Google Scholar 

  17. Zhang C, Yan J, Li C, Bie R. Contour detection via stacking random forest learning. Neurocomputing, 2018, 275: 2702-2715.

    Article  Google Scholar 

  18. Shen Y, Lu H, Jia J. Classification of motor imagery EEG signals with deep learning models. In Proc. the 7th International Conference on Intelligent Science and Big Data Engineering, September 2017, pp.181-190.

  19. Dollár P, Zitnick C L. Fast edge detection using structured forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(8): 1558-1570.

    Article  Google Scholar 

  20. van den Bergh M, Boix X, Roig G, van Gool L. SEEDS: Superpixels extracted via energy-driven sampling. International Journal of Computer Vision, 2015, 111(3): 298-314.

    Article  MathSciNet  Google Scholar 

  21. Krizhevsky A. Learning multiple layers of features from tiny images. Technical Report, University of Toronto, 2009. http://www.cs.toronto.edu/_kriz/learning-features-2009-TR.pdf, March 2019.

  22. Lin T Y, Dollár P, Girshick R B, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.936-944.

  23. Chen L C, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611, 2018. https:// arxiv.org/abs/1802.02611, March 2019.

  24. Chen L C, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587, 2017. https://arxiv.org/abs/1706.05587, March 2019.

  25. Chollet F. Xception: Deep learning with depthwis separable convolutions. arXiv:1610.02357, 2016. https://arxiv.org/abs/1610.02357, March 2019.

  26. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. Caffe: Convolutional architecture for fast feature embedding. In Proc. the 22nd ACM International Conference on Multimedia, November 2014, pp.675-678.

  27. Tang M, Gorelick L, Veksler O, Boykov Y. GrabCut in one cut. In Proc. the 2013 IEEE International Conference on Computer Vision, December 2013, pp.1769-1776.

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai, 200444, China

    Han Liu, Hang Du & Dan Zeng

  2. Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai, 200444, China

    Han Liu, Hang Du & Dan Zeng

  3. Department of Computer Science, The University of Texas at San Antonio, San Antonio, USA

    Qi Tian

Authors
  1. Han Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hang Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qi Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dan Zeng.

Electronic supplementary material

ESM 1

(PDF 404 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Du, H., Zeng, D. et al. Cloud Detection Using Super Pixel Classification and Semantic Segmentation. J. Comput. Sci. Technol. 34, 622–633 (2019). https://doi.org/10.1007/s11390-019-1931-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-019-1931-y

Keywords

Search

Navigation