Spectral Unmixing in Multiple-Kernel Hilbert Space for Hyperspectral Imagery

• Published in: IEEE transactions on geoscience and remote sensing Vol. 51; no. 7; pp. 3968 - 3981
• Main Authors: Gu, Yanfeng, Wang, Shizhe, Jia, Xiuping
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied geophysics; Boundaries; Construction; Earth sciences; Earth, ocean, space; Exact sciences and technology; Hilbert space; Hyperspectral imagery; Hyperspectral imaging; Internal geophysics; Kernel; Kernels; Learning; Machine learning; Materials; multiple-kernel learning (MKL); reproducing kernel Hilbert space (RKHS); Spectra; spectral unmixing; State of the art; Studies; Support vector machines; support vector machines (SVMs); experimental studies; algorithms; multiple-kernel learning (MKL); spectral unmixing; reproducing kernel Hilbert space (RKHS); Hyperspectral imagery; imagery; accuracy; remote sensing; support vector machines (SVMs)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2012.2227757

In this paper, we address a spectral unmixing problem for hyperspectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of spectral unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperspectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass spectral differences and improve unmixing accuracy, compared to the state-of-the-art algorithms tested.