Superpixel-based graphical model for remote sensing image mapping

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 11, 2015, Pages 5861-5871

  Zhang, Guangyun ^a,b   Jia, Xiuping ^b,c,d   Hu, Jiankun ^b  

^a TIANJIN UNIVERSITY   (China)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c HARBIN ENGINEERING UNIVERSITY   (China)

^d NATIONAL ENGINEERING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE   (China)

Author keywords

Graphical model; remote sensing; superpixel

Indexed keywords

CLASSIFICATION (OF INFORMATION); GRAPHIC METHODS; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; MAPPING; PIXELS; REMOTE SENSING; SEMANTICS;

CLASSIFICATION FRAMEWORK; CLASSIFICATION PROCEDURE; CONTEXTUAL INFORMATION; GRAPHICAL MODEL; REMOTE SENSING IMAGE CLASSIFICATION; STATE-OF-THE-ART METHODS; SUPER PIXELS; WATERSHED SEGMENTATION ALGORITHM;

IMAGE CLASSIFICATION;

ACCURACY ASSESSMENT; GRADIENT ANALYSIS; GRAPHICAL METHOD; IMAGE CLASSIFICATION; IMAGE PROCESSING; NUMERICAL MODEL; PIXEL; REMOTE SENSING; WHITE NOISE;

EID: 85027941310     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2015.2423688     Document Type: Article

References (37)

1. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis, 4th ed. New York, NY, USA: Springer-Verlag, 2006.
2. D. Tuia, E. Merenyi, X. Jia, and M. Grana-Romay, "Foreword to the special issue on machine learning for remote sensing data processing," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 10007-10011, Apr. 2014.
3. J. A. Benediktsson, M. Pesaresi, and K. Arnason, "Classification and feature extraction for remote sensing images from urban areas based on morphological transformations," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 9, pp. 1940-1949, Sep. 2003.
4. J. A. Benediktsson, A. J. Palmason, and J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 480-491, 2005.
5. P. H. Swain, S. B. Vardeman, and J. C. Tilton, "Contextual classification of multispectral image data," Pattern Recognit., vol. 13, no. 6, pp. 429-441, 1981.
6. J. A. Richards and X. Jia, "A Dempster-Shafer relaxation approach to context classification," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5, pp. 1422-1431, May 2007.
7. 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. 721-740, Nov. 1984.
8. J. A. Richards and X. Jia, "Managing the spectral-spatial mix in context classification using Markov random fields," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 2, pp. 311-314, Apr. 2008.
9. 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.
10. C. A. Bouman and M. Shapiro, "A multiscale random field model for Bayesian image segmentation," IEEE Trans. Image Process., vol. 3, no. 2, pp. 162-177, Mar. 1994.
11. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labelling sequence data," in Proc. Int. Conf. ML, 2001, pp. 282-289.
12. S. Kumar, "Models for learning spatial interactions in natural images for context-based classification," Ph.D. dissertation, Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA, 2005.
13. P. Zhong and R. Wang. "A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 3978-3988, Dec. 2007.
14. 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.
15. W. L. Lu, K. P. Murphy, J. J. Little, A. Sheffer, and H. Fu, "A hybrid conditional random field for estimating the underlying ground surface from airborne Lidar data," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2913-2922, Aug. 2009.
16. J. D. Wegner, R. Hansch, A. Thiele, and U. Sorgel, "Building detection from one orthophoto and high-resolution InSAR data using conditional random fields," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 4, pp. 83-91, 2011.
17. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral image using watershed transformation," Pattern Recognit., vol. 43, no. 7, pp. 2367-2379, Jul. 2010.
18. Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, "Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2973-2987, Aug. 2009.
19. Y. Xiao, D. Tien, and X. Jia, "Region based perceptual grouping for road extraction from high resolution images," Int. J. Inf. Technol. Appl., vol. 9, no. 3/4, pp. 208-217, 2010.
20. T. Blaschke, "Object based image analysis for remote sensing," ISPRS J. Photogramm. Remote Sens., vol. 65, pp. 2-16, 2010.
21. A. Tzotsos, K. Karantzalos, and D. Argialas, "Object-based image analysis through nonlinear scale-space filtering," ISPRS J. Photogramm. Remote Sens., vol. 66, pp. 2-16, 2011.
22. A. Tzotsos and D. Argialas, Eds., "Support vector machine classification for object-based image analysis," in Object Based Image Analysis- Spatial Concepts for Knowledge Driven Remote Sensing Application. New York, NY, USA: Springer-Verlag, 2008, pp. 221-237.
23. O. Hall and G. J. Hay, "A multiscale object specific approach to digital change detection," Int. J. Appl. Earth Observ. Geoinf., vol. 4, pp. 311-327, 2003.
24. Y. O. Ouma, J. Tetuko, and R. Tateishi, "Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and nonforest vegetation in very high-resolution optical-sensor imagery," Int. J. Remote Sens., vol. 29, pp. 3417-3456, 2008.
25. D. Carvajalino, G. Sapiro, M. Velez-Reyes, and P. E. Castillo, "Multiscale representation and segmentation of hyperspectral imagery using geometric partial differential equations and algebraic multigrid methods," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 8, pp. 2418-2434, Aug. 2008.
26. 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.
27. J. Tighe and S. Lazebnik, "Superparsing: Scalable nonparametric image parsing with superpixels," in Proc. Eur. Conf. Comput. Vis., 2010, vol. 6315/2010, pp. 352-365.
28. 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.
29. L. Vincent and P. Soille, "Watersheds in digital spaces; an efficient algorithm based on immersion simulations," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 6, pp. 583-598, Jun. 1991.
30. L. Vincent, "Morphological grayscale reconstruction in image analysis: Application and efficient algorithms," IEEE Trans. Image Process., vol. 2, no. 2, pp. 176-201, Apr. 1993.
31. A. N. Evans and X. U. Liu, "A morphological gradient approach to color edge detection," IEEE Trans. Image Process., vol. 15, no. 6, pp. 1454-1463, Jun. 2006.
32. Z. Zhang, G. Dai, C. Xu, and M. I. Jordan, "Regularized discriminant analysis, ridge regression and beyond," J. Mach. Learn. Res., vol. 11, pp. 2199-2228, 2010.
33. Y. Y. Boykov andM.-P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," in Proc. Int. Conf. Comput. Vis., 2001, pp. 105-112.
34. J. S. Yedidia, W. T. Freeman, and Y. Weiss, "Constructing free-energy approximations and generalized belief propagation algorithms," IEEE Trans. Inf. Theory, vol. 51, no. 7, pp. 2282-2312, Jul. 2005.
35. D. J. C. Mackay, Information Theory, Inference, and Learning Algorithms. Cambridge, U.K.: Cambridge Univ. Press, 2003.
36. X. Jia and J. A. Richards, "Segmented principal components transformation for efficient hyperspectral remote sensing image display and classification," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 1, pp. 538-542, Jan. 1999.
37. D. C. Liu and J. Nocedal, "On the limited memory BFGS method for large scale optimization," Math. Programm., vol. 45, no. 3, pp. 503-528, Dec. 1989.