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Article

Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification

1
Electronics and Informatics Department (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2,1050 Brussels, Belgium
2
Interuniveristy Microelectronics Center (IMEC), Kapeldreef 75, BE-3001 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(8), 6727-6764; https://doi.org/10.3390/rs6086727
Received: 19 March 2014 / Revised: 25 June 2014 / Accepted: 30 June 2014 / Published: 24 July 2014
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF) consider both multi-scale and contextual information in a unified discriminative probabilistic framework, yet they suffer from two main drawbacks. On the one hand, their current classification performance still leaves space for improvement, mostly due to the use of very simple or inappropriate pairwise energy expressions to model complex spatial patterns; on the other hand, their training remains complex, particularly for multi-class problems. In this work, we investigated alternative pairwise energy expressions to better account for class transitions and developed an efficient parameters learning strategy for the resultant expression. We propose: (i) a multi-scale CRF model with novel energies that involves information related to the multi-scale image structure; and (ii) an efficient maximum margin parameters learning procedure where the complex learning problem is decomposed into simpler individual multi-class sub-problems. During experiments conducted on several well-known satellite image data sets, the suggested multi-scale CRF exhibited between a 1% and 15% accuracy improvement compared to other works. We also found that, on different multi-scale decompositions, the total number of regions and their average size have a direct impact on the classification results. View Full-Text
Keywords: conditional random fields (CRF); multi-class maximum margin; standard piecewise training; image segmentation; image classification conditional random fields (CRF); multi-class maximum margin; standard piecewise training; image segmentation; image classification
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MDPI and ACS Style

Alioscha-Perez, M.; Sahli, H. Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification. Remote Sens. 2014, 6, 6727-6764. https://doi.org/10.3390/rs6086727

AMA Style

Alioscha-Perez M, Sahli H. Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification. Remote Sensing. 2014; 6(8):6727-6764. https://doi.org/10.3390/rs6086727

Chicago/Turabian Style

Alioscha-Perez, Mitchel, and Hichem Sahli. 2014. "Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification" Remote Sensing 6, no. 8: 6727-6764. https://doi.org/10.3390/rs6086727

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