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Title:
Remote sensing image segmentation based on Dynamic Statistical Region Merging
Author: Huang, Zhijian (1) ; Zhang, Jinfang (2) ; Li, Xiang (1) ; Zhang, Hui (2)
Corresponding Author: Huang, Z.(zhijian07@iscas.ac.cn)
Keyword: Statistical Region Merging ; Multi-scale segmentation ; Remote sensing image ; Region growing
Source: Optik
Issued Date: 2014
Volume: 125, Issue:2, Pages:870-875
Indexed Type: SCI ; EI
Department: (1) School of Electronic Science and Engineering, National University of Defense Technology, China; (2) Science and Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Science, China
Abstract: Using the Statistical Region Merging (SRM) for remote sensing image segmentation, we found the result is unsatisfactory. To improve segmentation accuracy and the correctness, this paper proposed a Dynamic Statistical Region Merging (DSRM). It tries to let the most similar regions to be tested first. At first, it redefines the dissimilarity based-on regions. Then, it dynamically updates the dissimilarity and adjusts the test order during the procedure of merging. Experiments demonstrate the accuracy of the DSRM is higher than the SRM and its computational complexity is approximately linear. In addition, we extend the DSRM to multi-band remote sensing image and use it for multi-scale segmentation. © 2013 Elsevier GmbH.
English Abstract: Using the Statistical Region Merging (SRM) for remote sensing image segmentation, we found the result is unsatisfactory. To improve segmentation accuracy and the correctness, this paper proposed a Dynamic Statistical Region Merging (DSRM). It tries to let the most similar regions to be tested first. At first, it redefines the dissimilarity based-on regions. Then, it dynamically updates the dissimilarity and adjusts the test order during the procedure of merging. Experiments demonstrate the accuracy of the DSRM is higher than the SRM and its computational complexity is approximately linear. In addition, we extend the DSRM to multi-band remote sensing image and use it for multi-scale segmentation. © 2013 Elsevier GmbH.
Language: 英语
WOS ID: WOS:000329256800056
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16888
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Huang, Zhijian ,Zhang, Jinfang ,Li, Xiang ,et al. Remote sensing image segmentation based on Dynamic Statistical Region Merging[J]. Optik,2014-01-01,125(2):870-875.
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