Orthogonal Nonnegative Matrix Factorization Combining Multiple Features for Spectral-Spatial Dimensionality Reduction of Hyperspectral Imagery

• Published in: IEEE transactions on geoscience and remote sensing Vol. 54; no. 7; pp. 4272 - 4286
• Main Authors: Jinhuan Wen, Fowler, James E., Mingyi He, Yong-Qiang Zhao, Chengzhi Deng, Menon, Vineetha
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computers; Convergence; Factorization; Feature extraction; Hyperspectral imaging; Imagery; multiple features; Optimization; orthogonal nonnegative matrix factorization (NMF); Principal component analysis; Production methods; Reduction; Spectra; spectral-spatial dimensionality reduction; Surface layer; Texture; orthogonal nonnegative matrix factorization (NMF); Feature extraction; spectral–spatial dimensionality reduction; multiple features
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2016.2539154

Nonnegative matrix factorization (NMF), which can lead to nonsubtractive parts-based representation, has been demonstrated to be effective for dimensionality reduction of hyperspectral imagery (HSI). However, existing NMF methods applied to HSI use only a single spectral feature and do not take into consideration spatial information, such as texture or morphological features, while it has been widely acknowledged that exploiting multiple features can improve performance. Consequently, a variant of orthogonal NMF, which can not only achieve a nonnegative factorization but also exploit the complementary information that arises among heterogeneous features, is proposed for hyperspectral dimensionality reduction. The proposed method, which couples orthogonal NMF with a previous multiple-features-combining algorithm, yields a discriminative low-dimensional feature representation that matches the intuition that parts should sum to produce a whole. An efficient multiplicative updating procedure is derived, and its local convergence is guaranteed theoretically. Experimental results on two hyperspectral data sets demonstrate the effectiveness of the proposed method.