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Remote Sens. 2017, 9(1), 96; doi:10.3390/rs9010096

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

1
Electronic and Information School, Wuhan University, Wuhan 430072, China
2
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Xiaofeng Li and Prasad S. Thenkabail
Received: 15 November 2016 / Revised: 9 January 2017 / Accepted: 16 January 2017 / Published: 22 January 2017
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Abstract

This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification. (1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents’ regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy. View Full-Text
Keywords: Synthetic Aperture Radar (SAR); image classification; semantic pyramid; Conditional Random Field (CRF); Bayesian Network (BN) Synthetic Aperture Radar (SAR); image classification; semantic pyramid; Conditional Random Field (CRF); Bayesian Network (BN)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

He, C.; Liu, X.; Feng, D.; Shi, B.; Luo, B.; Liao, M. Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field. Remote Sens. 2017, 9, 96.

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