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钟燕飞--A Hybrid Object-Oriented Conditional Random Field ..

发布日期:2014-08-29 10:48:34 阅读次数:[5704]次 作者:

核心提示:钟燕飞--A Hybrid Object-Oriented Conditional Random Field ..

标题:A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery
作者:Zhong, YF (Zhong, Yanfei); Zhao, J (Zhao, Ji); Zhang, LP (Zhang, Liangpei)
来源出版物:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 卷:52 期:11 页:7023-7037 DOI:10.1109/TGRS.2014.2306692 出版年:NOV 2014
Web of Science 核心合集中的 "被引频次":0
被引频次合计:0
摘要:High spatial resolution (HSR) remote sensing imagery provides abundant geometric and detailed information, which is important for classification. In order to make full use of the spatial contextual information, object-oriented classification and pairwise conditional random fields (CRFs) are widely used. However, the segmentation scale choice is a challenging problem in object-oriented classification, and the classification result of pairwise CRF always has an oversmooth appearance. In this paper, a hybrid object-oriented CRF classification framework for HSR imagery, namely, CRF + OO, is proposed to address these problems by integrating object-oriented classification and CRF classification. In CRF + OO, a probabilistic pixel classification is first performed, and then, the classification results of two CRF models with different potential functions are used to obtain the segmentation map by a connected-component labeling algorithm. As a result, an object-level classification fusion scheme can be used, which integrates the object-oriented classifications using a majority voting strategy at the object level to obtain the final classification result. The experimental results using two multispectral HSR images (QuickBird and IKONOS) and a hyperspectral HSR image (HYDICE) demonstrate that the proposed classification framework has a competitive quantitative and qualitative performance for HSR image classification when compared with other state-of-the-art classification algorithms.
地址:[Zhong, Yanfei; Zhao, Ji; Zhang, Liangpei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.
通讯作者地址:Zhong, YF (通讯作者),Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.
电子邮件地址:zhongyanfei@whu.edu.cn; zhaoji2015@gmail.com; zlp62@whu.edu.cn


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