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�������պ����ѧѧ�� 2005, Vol. 31 Issue (10) :1101-1105    DOI:
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Noise robust ICA feature extraction algorithm for hyperspectral image
Du Peng, Zhao Huijie*
School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

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ժҪ ������ȡ�Ǹ߹�������Ӧ�õ�һ����Ҫ����,���ڽ��߹��������о����������ʵĵ�����������ȥ��������Ϣ.�����һ��ʹ�ö����ɷַ���(ICA,Independent Component Analysis)���и߹���ң�е���������ȡ�ķ���.Ϊ�˽��ICA�������������е�����,���������������(MNF,Maximum Noise Fraction)�㷨�����ͳ�����ɷַ��������������봦��,��MNF�����IJ���ȫ�����ɷַ���(UICA,Undercomplete ICA)�ڲ�����������ȡ������������ܹ���úܸߵ�����Ч��.������HYDICE��PHI������������,�ֱ�������㷨��ʱ��Ч�ʺ�������ȡ�������������,֤���˸��㷨����Ԥ�ڵ�����.
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Abstract�� Feature extraction is important to hyperspectral imagery processing in that it can distinguish special featured object from background clutter and remove redundant information. An ICA(independent component analysis) based on the feature extraction algorithm for hyperspectral remote sensing data is proposed. In order to handle the over-sensitivity of ICA to noise and data imperfection, the MNF(maximum noise fraction) is adopted as the replacement of conventional principal component analysis. The UICA(undercomplete ICA) led by the MNF not only raises the time efficiency, but also maintains the extracting ability of ICA. The performance of the algorithm is verified by the results of HYIDCE and PHI experiments.
Keywords�� hyperspectral remote sensing   feature extraction   independent component analysis   noise robust   maximum noise fraction     
Received 2004-06-15;
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About author: �� ��(1976-),��,����������,˶ʿ��, pengdu@pengdu.net.
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����, �Ի۽�.���ڿ�����ICA�ĸ߹�������������ȡ����[J]  �������պ����ѧѧ��, 2005,V31(10): 1101-1105
Du Peng, Zhao Huijie.Noise robust ICA feature extraction algorithm for hyperspectral image[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2005,V31(10): 1101-1105
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