期刊
REMOTE SENSING
卷 9, 期 4, 页码 -出版社
MDPI AG
DOI: 10.3390/rs9040323
关键词
hyperspectral data; dimensionality reduction; graph embedding; spectral similarity
类别
资金
- National Natural Science Foundation of China [NSFC-91638201, 61571033]
- Higher Education and High-Quality and World-Class Universities [PY201619]
Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS) measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA).
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