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�������պ����ѧѧ�� 2009, Vol. 35 Issue (5) :544-546    DOI:
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1. �������պ����ѧ ������Ϣ����ѧԺ, ���� 100191;
2. �������պ����ѧ ����������������ص�ʵ����, ���� 100191
Unsupervised classification approach based on graph-segment for multispectral remote sensing images
Liu Nana1, Li Jingwen1, Li Ning2*
1. School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
2. State Key Laboratory of Software Development Environment , Beijing University of Aeronautics and Astronautics,Beijing 100191, China

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Abstract�� To solving the noisy points and high cost problems of pixel-based multispectral image classification, a hybrid unsupervised approach with graph-based segment and fuzzy c-means clustering was presented. First, based on the relationships among neighboring pixels, image was segmented into groups of sub-regions using the graph-based algorithm. Then according to the global feature vector of sub-region, the fuzzy c-means classifier was used to obtain the classification map. Experiments turn out that the proposed approach, which considers both relationships of neighboring pixels and global feature of sub-region, can achieve better accuracy and efficiency by comparing the result with pixel-based fuzzy c-means classification.
Keywords�� multispectral image   multispectral classification   unsupervised classification     
Received 2008-08-10;
About author: ������(1984-),Ů,�ӱ���ˮ��,˶ʿ��,liunana@ee.buaa.edu.cn.
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������, ���, �� ��.����ͼ�۷ָ�Ķ����ͼ��Ǽල���෽��[J]  �������պ����ѧѧ��, 2009,V35(5): 544-546
Liu Nana, Li Jingwen, Li Ning.Unsupervised classification approach based on graph-segment for multispectral remote sensing images[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2009,V35(5): 544-546
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