Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field
AbstractThis 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
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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.
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 Sensing. 2017; 9(1):96.Chicago/Turabian Style
He, Chu; Liu, Xinlong; Feng, Di; Shi, Bo; Luo, Bin; Liao, Mingsheng. 2017. "Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field." Remote Sens. 9, no. 1: 96.
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