SAR Image Classification Based on CRFs With Integration of Local Label Context and Pairwise Label Compatibility
SAR Image Classification Based on CRFs With Integration of Local Label Context and Pairwise Label Compatibility
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基于局部标签上下文集成和成对标签兼容性的条件随机场的 SAR 图像分类
DOI:
10.1109/jstars.2013.2262038
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发表时间:
2014
期刊:
影响因子:
--
通讯作者:
Wenxian Yu
中科院分区:
文献类型:
--
作者:
Yongke Ding;Yuanxiang Li;Wenxian Yu
Context information plays a critical role in SAR image classification, as high-resolution SAR data provides more information on scene context and visual structures. This paper presents a novel classification method for SAR images based on conditional random fields (CRFs) with integration of low-level features, local label context, and pairwise label compatibility. First, we extract the low-level features used in the SVM-based unary classifier for SAR images. The supertexture is newly introduced as one of the low-level features to model the texture context between image patches. Then, we describe the context information, including local context potential and pairwise potential. Incorporation of the category context helps to resolve the ambiguities of the unary classifier. The performance of our approach in both accuracy and visual appearance for high-resolution SAR image classification is proved in the experiments.
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