High-resolution image classification integrating spectral-spatial-location cues by conditional random fields

IEEE Transactions on Image Processing

Volumn 25, Issue 9, 2016, Pages 4033-4045

  Zhao, Ji ^a   Zhong, Yanfei ^a   Shu, Hong ^a   Zhang, Liangpei ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Conditional random fields; high resolution; image classification; remote sensing; spatial contextual information; spatial location

Indexed keywords

ALGORITHMS; CHARACTER RECOGNITION; CLASSIFICATION (OF INFORMATION); IMAGE RECONSTRUCTION; INFORMATION USE; LOCATION; PIXELS; RANDOM PROCESSES; REMOTE SENSING;

CLASSIFICATION ALGORITHM; CONDITIONAL RANDOM FIELD; CONDITIONAL RANDOM FIELDS(CRFS); CONTEXTUAL INFORMATION; HIGH RESOLUTION; HIGH RESOLUTION REMOTE SENSING IMAGERY; HIGHER ORDER POTENTIALS; SPATIAL LOCATION;

IMAGE CLASSIFICATION;

EID: 84980000082     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2016.2577886     Document Type: Article

References (47)

1. G. Moser, S. B. Serpico, and J. A. Benediktsson, "Land-cover mapping by Markov modeling of spatial-contextual information in veryhigh-resolution remote sensing images," Proc. IEEE, vol. 101, no. 3, pp. 631-651, Mar. 2013.
2. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
3. Y. Zhong, Q. Zhu, and L. Zhang, "Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 11, pp. 6207-6222, Nov. 2015.
4. J. D. Paola and R. A. Schowengerdt, "A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification," IEEE Trans. Geosci. Remote Sens., vol. 33, no. 4, pp. 981-996, Jul. 1995.
5. D. Tao, X. Li, X. Wu, and S. J. Maybank, "Geometric mean for subspace selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 260-274, Feb. 2009.
6. T. Zhou and D. Tao, "Double shrinking sparse dimension reduction," IEEE Trans. Image Process., vol. 22, no. 1, pp. 244-257, Jan. 2013.
7. M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, "Advances in spectral-spatial classification of hyperspectral images," Proc. IEEE, vol. 101, no. 3, pp. 652-675, Mar. 2013.
8. K. Schindler, "An overview and comparison of smooth labeling methods for land-cover classification," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 11, pp. 4534-4545, Nov. 2012.
9. X. Kang, S. Li, and J. A. Benediktsson, "Spectral-spatial hyperspectral image classification with edge-preserving filtering," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2666-2677, May 2014.
10. X. Kang, S. Li, L. Fang, M. Li, and J. A. Benediktsson, "Extended random walker-based classification of hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 144-153, Jan. 2015.
11. T. Blaschke, "Object based image analysis for remote sensing," ISPRS J. Photogram. Remote Sens., vol. 65, no. 1, pp. 2-16, Jan. 2010.
12. Y. Zhong, B. Zhao, and L. Zhang, "Multiagent object-based classifier for high spatial resolution imagery," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 841-857, Feb. 2014.
13. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using watershed transformation," Pattern Recognit., vol. 43, no. 7, pp. 2367-2379, 2010.
14. 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.
15. M. Baatz and A. Schape, "Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation," in Angewandte Geographische Informationsverarbeitung XII, J. Strobl, T. Blaschke, and G. Griesebner, Eds. Heidelberg, Germany: Wichmann-Verlag, 2000, pp. 12-23.
16. B. Johnson and Z. Xie, "Unsupervised image segmentation evaluation and refinement using a multi-scale approach," ISPRS J. Photogram. Remote Sens., vol. 66, no. 4, pp. 473-483, Jul. 2011.
17. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-6, no. 6, pp. 721-741, Nov. 1984.
18. Y. Tarabalka, M. Fauvel, J. Chanussot, and J. A. Benediktsson, "SVMand MRF-based method for accurate classification of hyperspectral images," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 736-740, Oct. 2010.
19. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Hyperspectral image segmentation using a new Bayesian approach with active learning," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3947-3960, Oct. 2011.
20. J. D. Lafferty, A. McCallum, and F. C. N. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proc. 18th Int. Conf. Mach. Learn., 2001, pp. 282-289.
21. S. Kumar and M. Hebert, "Discriminative random fields: A discriminative framework for contextual interaction in classification," in Proc. 9th Int. Conf. Comput. Vis., Oct. 2003, pp. 1150-1157.
22. S. Kumar and M. Hebert, "Discriminative random fields," Int. J. Comput. Vis., vol. 68, no. 2, pp. 179-201, 2006.
23. G. Zhang and X. Jia, "Simplified conditional random fields with class boundary constraint for spectral-spatial based remote sensing image classification," IEEE Geosci. Remote Sens. Lett., vol. 9, no. 5, pp. 856-860, Sep. 2012.
24. P. Zhong and R. Wang, "Learning conditional random fields for classification of hyperspectral images," IEEE Trans. Image Process., vol. 19, no. 7, pp. 1890-1907, Jul. 2010.
25. P. Zhong and R. Wang, "Modeling and classifying hyperspectral imagery by CRFs with sparse higher order potentials," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 2, pp. 688-705, Feb. 2011.
26. Y. Zhong, X. Lin, and L. Zhang, "A support vector conditional random fields classifier with a Mahalanobis distance boundary constraint for high spatial resolution remote sensing imagery," IEEE J. Sel. Topics Appl. Earth Observat. Remote Sens., vol. 7, no. 4, pp. 1314-1330, Apr. 2014.
27. Y. Zhong, J. Zhao, and L. Zhang, "A hybrid object-oriented conditional random field classification framework for high spatial resolution remote sensing imagery," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 7023-7037, Nov. 2014.
28. J. Zhao, Y. Zhong, and L. Zhang, "Detail-preserving smoothing classifier based on conditional random fields for high spatial resolution remote sensing imagery," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2440-2452, May 2015.
29. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 844-856, Feb. 2013.
30. J. Zhao, Y. Zhong, Y. Wu, L. Zhang, and H. Shu, "Sub-pixel mapping based on conditional random fields for hyperspectral remote sensing imagery," IEEE J. Sel. Topics Signal Process., vol. 9, no. 6, pp. 1049-1060, Sep. 2015.
31. P. Kohli and C. Rother, "Higher-order models in computer vision," in Image Processing and Analysis with Graphs. Boca Raton, FL, USA: CRC Press, 2012, pp. 1-28.
32. Y. Li and D. P. Huttenlocher, "Sparse long-range random field and its application to image denoising," in Proc. Eur. Conf. Comput. Vis., 2008, pp. 344-357.
33. S. Roth and M. J. Black, "Fields of experts," Int. J. Comput. Vis., vol. 82, no. 2, pp. 205-229, Apr. 2009.
34. P. Kohli, M. P. Kumar, and P. H. S. Torr, "P3 & beyond: Solving energies with higher order cliques," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2007, pp. 1-8.
35. S. Vicente, V. Kolmogorov, and C. Rother, "Joint optimization of segmentation and appearance models," in Proc. 12th Int. Conf. Comput. Vis., Sep./Oct. 2009, pp. 755-762.
36. P. Kohli and P. H. S. Torr, "Robust higher order potentials for enforcing label consistency," Int. J. Comput. Vis., vol. 82, no. 3, pp. 302-324, May 2009.
37. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, Apr. 2011, Art. no. 27.
38. T.-F. Wu, C.-J. Lin, and R. C. Weng, "Probability estimates for multiclass classification by pairwise coupling," J. Mach. Learn. Res., vol. 5, pp. 975-1005, Dec. 2004.
39. C. Rother, V. Kolmogorov, and A. Blake, "GrabCut: Interactive foreground extraction using iterated graph cuts," ACM Trans. Graph., vol. 23, no. 3, pp. 309-314, Aug. 2004.
40. J. Shotton, J. Winn, C. Rother, and A. Criminisi, "TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation," in Proc. 9th Eur. Conf. Comput. Vis., 2006, pp. 1-15.
41. B. Georgescu, I. Shimshoni, and P. Meer, "Mean shift based clustering in high dimensions: A texture classification example," in Proc. 9th Int. Conf. Comput. Vis., Oct. 2003, pp. 456-463.
42. S. Z. Li, Markov Random Field Modeling in Image Analysis, 3rd ed. New York, NY, USA: Springer-Verlag, 2009.
43. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
44. R. Szeliski, et al., "A comparative study of energy minimization methods for Markov random fields with smoothness-based priors," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 6, pp. 1068-1080, Jun. 2008.
45. V. Kolmogorov and R. Zabin, "What energy functions can be minimized via graph cuts?" IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 2, pp. 147-159, Feb. 2004.
46. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction, 4th ed. New York, NY, USA: Springer-Verlag, 2006.
47. G. Licciardi, et al., "Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3857-3865, Nov. 2009.