Solberg A H S, Taxt T, Jain A K. A Markov random field model for classification of multisource satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(1): 100-113.
[2]
Jackson Q, Landgrebe D. Adaptive Bayesian contextual classification based on Markov randomfields[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2454-2463.
[3]
Tranni G, Gamba P. Boundry adaptive MRF classification of optical very high resolution images[C]//IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE Press, 2007: 1493-1496.
[4]
Lafferty J, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data[C]//the 18th International Conference on Machine Learning. Brookline, United States: Microtome Publishing, 2001: 282-289.
[5]
Kumar S, Hebert M. Discriminative random fields: A discriminative framework for contextual interaction in classification[C]//IEEE International Conference on Computer Vision. Piscataway, NJ: IEEE Press, 2003: 1150-1157.
[6]
Kumar S, Hebert M. Discriminative random fields[J]. International Journal of Computer Vision, 2006, 68(2): 179-201.
[7]
Kumar S. Models for learning spatial interactions in natural images for context-based classification[D]. Pittsburgh: Carnegie Mellon University School of Computer Science, 2005.
[8]
Liu C, Szeliski R, Kang S B, et al. Automatic estimation and removal of noise from a single image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 299-314.
[9]
Zhong P, Wang R S. A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(12): 3978-3988.
[10]
Zhong P, Wang R S. Learning conditional random fields for classification of hyperspectral images[J]. IEEE Transactions on Image Processing, 2010, 19(7): 1890-1907.
[11]
He X M, Zemel R, Carreira-Perpinan M A. Multi-scale conditional random fields for image labelling[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE Press, 2004: 695-702.
[12]
Shotton J, Winn J, Rother C, et al. Texton boost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation[C]//European Conference on Computer Vision. Berlin, Germany: Springer Verlag, 2006: 1-15.
[13]
Shotton J, Winn J, Rother C, et al. Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context[J]. International Journal of Computer Vision, 2009, 81(1): 2-23.
[14]
Julesz B. Textons, the elements of texture perception, and their interactions[J]. Nature, 1981, 290(5802): 91-97.
[15]
Torralba A, Murphy K P, Freeman W T. Sharing visual features for multi-class and multi-view object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 19(5): 854-869.
[16]
Boykov Y, Jolly M P. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images[C]//Proceedings of International Conference on Computer Vision. Piscataway, NJ: IEEE Press, 2001: 105-112.
[17]
Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239.
[18]
Friedman J, Hastie T, Tibshirani R. Additive logistic regression: A statistical view of boosting[J]. Annals of Statistics, 2000, 28(2): 337-374.
[19]
Jones D G, Malik J. A computational framework for determining stereo correspondence from a set of linear spatial filters[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer Verlag, 1992: 395-410.
[20]
Rother C, Kolmogorov V, Blake A. GrabCut—Interactive foreground extraction using iterated graph cuts[J]. ACM Transactions on Graphics, 2004, 23(3): 309-314.
[21]
Sutton C, McCallum A. Piecewise training of undirected models[C]//Proceedings of Conference on Uncertainty in Artificial Intelligence. Arlington, United States: AUAI Press, 2005: 568-575.
[22]
Baluja S, Rowley H A. Boosting sex identification performance[J]. International Journal of Computer Vision, 2006, 71(1): 111-119.
[23]
Zhu H, Basir O. An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1874-1889.
[24]
Bruzzone L, Roli F, Serpico S B. An experimental comparison of neural networks for the classification of multi-sensor remote sensing images[C]//Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE Press, 1995: 452-454.
[25]
Iikura Y, Chi T M, Msuoka Y. Efficient classification of multispectral images by a best linear discriminant function[C]//Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE Press, 1988: 505-508.
[26]
Tarabalka Y, Rana A. Graph-cut-based model for spectral-spatial classification of hyperspectral images[C]//Geoscience and Remote Sensing Symposium. Piscataway, NJ:IEEE Press, 2014: 3418-3421.