光谱学与光谱分析 |
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An Improved N-FINDR Endmember Extraction Algorithm Based on Manifold Learning and Spatial Information |
TANG Xiao-yan1, 2, GAO Kun1*, NI Guo-qiang1, ZHU Zhen-yu1, CHENG Hao-bo1 |
1. Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, Beijing Institute of Technology, Beijing 100081, China 2. School of Electronics and Electrical Engineering, Nanyang Institute of Technology, Nanyang 473004, China |
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Abstract An improved N-FINDR endmember extraction algorithm by combining manifold learning and spatial information is presented under nonlinear mixing assumptions. Firstly, adaptive local tangent space alignment is adapted to seek potential intrinsic low-dimensional structures of hyperspectral high-diemensional data and reduce original data into a low-dimensional space. Secondly, spatial preprocessing is used by enhancing each pixel vector in spatially homogeneous areas, according to the continuity of spatial distribution of the materials. Finally, endmembers are extracted by looking for the largest simplex volume. The proposed method can increase the precision of endmember extraction by solving the nonlinearity of hyperspectral data and taking advantage of spatial information. Experimental results on simulated and real hyperspectral data demonstrate that the proposed approach outperformed the geodesic simplex volume maximization (GSVM), vertex component analysis (VCA) and spatial preprocessing N-FINDR method (SPPNFINDR).
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Received: 2013-02-28
Accepted: 2013-05-06
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Corresponding Authors:
GAO Kun
E-mail: gaokun@bit.edu.cn
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