期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 7, 期 6, 页码 2035-2043出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2013.2290316
关键词
Classification; dimension reduction; hyperspectral image (HSI); linear discriminant analysis (LDA); spectral-spatial
类别
资金
- Research Grants of University of Macau [MYRG205(Y1-L4)-FST11-TYY, MYRG187(Y1-L3)-FST11-TYY, SRG010-FST11-TYY]
- Science and Technology Development Fund (FDCT) of Macau [FDCT-100-2012-A3]
- National Natural Science Foundation of China [61273244]
This paper proposes a spectral-spatial linear discriminant analysis (LDA) method for the hyperspectral image classification. A natural assumption is that similar samples have similar structure in the dimensionality reduced feature space. The proposed method uses a local scatter matrix from a small neighborhood as a regularizer incorporated into the objective function of LDA. Different from traditional LDA and its variants, our proposed method yields a self-adaptive projection matrix for dimension reduction, which improves the classification accuracy and avoids running out of memory. In order to consider the nonlinear case, this paper generalizes our linear version to its kernel version. Experimental results demonstrate that our proposed methods outperform several dimension reduction algorithms.
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