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�������պ����ѧѧ�� 2010, Vol. 36 Issue (6) :659-662    DOI:
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Extraction of saliency-gist features and target detection for remote sensing images

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Itti Laurent
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Abstract��

An automatic approach to detect and classify targets in high-resolution broad-area remote sensing images is explored, which relies on detecting statistical signatures of targets, in terms of a set of  biologically-inspired low��level visual features. The broad-area remote sensing images were first cut into small image chips with slide window, which were analyzed in two complementary ways: attention/saliency analysis exploits local features and their interactions across space, while gist analysis focuses on global non-spatial features and their statistics. Both saliency and gist feature sets were used to classify each chip as containing target or not, through using a support vector machine. The proposed algorithm outperformed the state-of-the-art HMAX algorithm in the experiments and thus can be used to reliably and effectively detect highly variable target objects in large scale remote sensing image datasets.

Keywords�� target detection   saliency feature   gist feature   remote sensing image     
Received 2009-04-27;
Fund:

������Ȼ��ѧ����������Ŀ(60875072)�����Ҹ߼����о���չ(863)�ƻ��ص�������Ŀ(2008AA12A200)

About author: ��־�ɣ�1983-�����У����ϻ����ˣ���ʿ��, lzcbuaa@gmail.com.
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��־��, ������, Itti Laurent.ң��ͼ�������-��Ҫ������ȡ��Ŀ����[J]  �������պ����ѧѧ��, 2010,V36(6): 659-662
Li Zhicheng, Qin Shiyin, Itti Laurent.Extraction of saliency-gist features and target detection for remote sensing images[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2010,V36(6): 659-662
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http://bhxb.buaa.edu.cn//CN/     ��     http://bhxb.buaa.edu.cn//CN/Y2010/V36/I6/659
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