Multilevel cloud detection in remote sensing images based on deep learning

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 10, Issue 8, 2017, Pages 3631-3640

  Xie, Fengying ^a   Shi, Mengyun ^a   Shi, Zhenwei ^a   Yin, Jihao ^a   Zhao, Danpei ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

Cloud detection; convolutional neural network (CNN); deep learning; remote sensing images; superpixel

Indexed keywords

DEEP LEARNING; DEEP NEURAL NETWORKS; IMAGE PROCESSING; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; ITERATIVE METHODS; NEURAL NETWORKS; PIXELS; SPACE OPTICS;

CLOUD DETECTION METHOD; CONVOLUTIONAL NEURAL NETWORK; INITIAL CLUSTER CENTERS; ITERATIVE CLUSTERING; MULTI-LEVEL DETECTION; MULTI-SCALE FEATURES; REMOTE SENSING IMAGE PROCESSING; REMOTE SENSING IMAGES;

REMOTE SENSING;

ARTIFICIAL NEURAL NETWORK; CLOUD COVER; DETECTION METHOD; IMAGE ANALYSIS; MACHINE LEARNING; PIXEL; REMOTE SENSING;

EID: 85018512559     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2017.2686488     Document Type: Article

References (33)

1. G. W. Paltridge, and C. M. R. Platt, Radiative Processes in Meteorology and Climatology. New York, NY, USA: Elsevier, 1976.
2. R. Saunders, "An automated scheme for the removal of cloud contamination from AVHRR radiances over western europe," Int. J. Remote Sens, vol. 7, no. 7, pp. 867-886, 1986.
3. R. W. Saunders, and K. T. Kriebel, "An improved method for detecting clear sky and cloudy radiances from AVHRR data," Int. J. Remote Sens, vol. 9, no. 1, pp. 123-150, 1988.
4. J. Cihlar, and J. Howarth, "Detection and removal of cloud contamination from AVHRR images," IEEE Trans. Geosci. Remote Sens, vol. 32, no. 3, pp. 583-589, May 1994.
5. L. D. Girolamo, and R. Davies, "The image navigation cloud mask for the multiangle imaging spectroradiometer (MISR)," J. Atmos. Ocean. Technol, vol. 12, no. 6, pp. 1215-1228, 1995.
6. G. J. Jedlovec, S. L. Haines, and F. J. LaFontaine, "Spatial and temporal varying thresholds for cloud detection in goes imagery," IEEE Trans. Geosci. Remote Sens, vol. 46, no. 6, pp. 1705-1717, Jun. 2008.
7. S. Le Hégarat-Mascle, and C. André, "Use of Markov random fields for automatic cloud/shadow detection on high resolution optical images," ISPRS J. Photogramm. Remote Sens, vol. 64, no. 4, pp. 351-366, 2009.
8. R. Rossi, R. Basili, F. Del Frate, M. Luciani, and F. Mesiano, "Techniques based on support vectormachines for cloud detection on quickbird satellite imagery," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2011, pp. 515-518.
9. P. Li, L. Dong, H. Xiao, and M. Xu, "A cloud image detection method based on SVM vector machine," Neurocomputing, vol. 169, pp. 34-42, 2015.
10. Y. Yuan, and X. Hu, "Bag-of-words and object-based classification for cloud extraction from satellite imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens, vol. 8, no. 8, pp. 4197-4205, Aug. 2015.
11. Q. Zhang, and C. Xiao, "Cloud detection of RGB color aerial photographs by progressive refinement scheme," IEEE Trans. Geosci. Remote Sens, vol. 52, no. 11, pp. 7264-7275, Nov. 2014.
12. Z. An, and Z. Shi, "Scene learning for cloud detection on remote-sensing images," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens, vol. 8, no. 8, pp. 4206-4222, Aug. 2015.
13. F. Melgani, G. Mercier, L. Lorenzi, and E. Pasolli, "Recent methods for reconstructingmissing data in multispectral satellite imagery," in Applications+ Practical Conceptualization+ Mathematics= Fruitful Innovation. New York, NY, USA: Springer, 2016, pp. 221-234.
14. H. Zhu, and G. Wan, "Local contrast preserving technique for the removal of thin cloud in aerial image," Optik-Int. J. Light Electron Opt, vol. 127, no. 2, pp. 742-747, 2016.
15. R. Achanta, A. Shaji, K. Smith,A. Lucchi, P. Fua, and S. Suösstrunk, "SLIC superpixels compared to state-of-The-Art superpixel methods," IEEE Trans. Pattern Anal. Mach. Intell, vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
16. C. L. Zitnick, and S. B. Kang, "Stereo for image-based rendering using image over-segmentation," Int. J. Comput. Vis, vol. 75, no. 1, pp. 49-65, 2007.
17. Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum, "Lazy snapping," ACM Trans. Graph, vol. 23, no. 3, pp. 303-308, 2004.
18. G. Mori, "Guiding model search using segmentation," in Proc. 10th IEEE Int. Conf. Comput. Vision, 2005, vol. 2, pp. 1417-1423.
19. B. Fulkerson, et al., "Class segmentation and object localization with superpixel neighborhoods," in Proc. IEEE 12th Int. Conf. Comput. Vision, 2009, vol. 9, pp. 670-677.
20. E. Welch, R. Moorhead, and J. Owens, "Image processing using the HSI color space," in Proc. IEEE Southeastcon, 1991, pp. 722-725.
21. A. R. Weeks, and G. E. Hague, "Color segmentation in the HSI color space using the k-means algorithm," Proc. SPIE, vol. 3026, pp. 143-154, 1997.
22. P. F. Felzenszwalb, and D. P. Huttenlocher, "Efficient graph-based image segmentation," Int. J. Comput. Vision, vol. 59, no. 2, pp. 167-181, 2004.
23. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proc. Int. Conf. Adv. Neural Inf. Process. Syst., 2012, pp. 1097-1105.
24. R. Zhao, W. Ouyang, H. Li, and X. Wang, "Saliency detection by multicontext deep learning," in Proc. IEEE Conf.Comput. Vision Pattern Recognit., 2015, pp. 1265-1274.
25. R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2014, pp. 580-587.
26. C. Dong, C. C. Loy, K. He, and X. Tang, "Learning a deep convolutional network for image super-resolution," in Proc. Eur. Conf. Comput. Vision, 2014, pp. 184-199.
27. V. Nair, and G. E. Hinton, "Rectified linear units improve restricted Boltzmann machines," in Proc. 27th Int.Conf.Mach. Learn., 2010, pp. 807-814.
28. N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," J. Mach. Learn. Res, vol. 15, no. 1, pp. 1929-1958, 2014.
29. Y. Jia, et al., "Caffe: Convolutional architecture for fast feature embedding," in Proc. 22nd ACM Int. Conf. Multimedia, 2014, pp. 675-678.
30. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, "An efficient k-means clustering algorithm: Analysis and implementation," IEEE Trans. Pattern Anal. Mach. Intell, vol. 24, no. 7, pp. 881-892, Jul. 2002.
31. D. Comaniciu, and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell, vol. 24, no. 5, pp. 603-619, May 2002.
32. T. F. Chan, and L. A. Vese, "Active contours without edges," IEEE Trans. Image Process, vol. 10, no. 2, pp. 266-277, Feb. 2001.
33. L. A. Vese, and T. F. Chan, "A multiphase level set framework for image segmentation using theMumford and Shahmodel," Int. J. Comput. Vision, vol. 50, no. 3, pp. 271-293, 2002.