Improved Low-Rank Matrix Approximation for Hyperspectral Image Spatial-Spectral Feature Extraction

Abstract:

It is important to take account into both the spectral domain and spatial domain information for hyperspectral image analysis. Thus, how to effectively integrate both spectral and spatial information confronts us. Motived by the least square form of PCA, we extend it to a low-rank matrix approximation form for multi-feature dimensionality redu-ction. In addition, we use the ensemble manifold regularize-ation techniques to capture the complementary information provided by spectral-spatial features of hyperspectral image. Experimental results on public hyperspectral data set demonstrate the effectiveness of our proposed method.

• Periodical: Applied Mechanics and Materials (Volume 590)
• Pages: 716-721
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.590.716
• Online since: June 2014
• Authors: Qian Zhang*, Yi Ping Yang, Xin Wei Jiang
• Keywords: Feature Extaction, Hyperspectral Classification, Low-Rank Matrix Approximation, Manifolding Learning, Multi-Feature

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