Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61888
Title: Multidomain subspace classification for hyperspectral images
Authors: Zhang, L
Zhu, X
Zhang, L 
Du, B
Issue Date: 2016
Source: IEEE transactions on geoscience and remote sensing, 2016, v. 54, no. 10, 7508922, p. 6138-6150
Abstract: Hyperspectral imaging offers new opportunities for pattern recognition tasks in the remote sensing community through its improved discrimination in the spectral domain. However, such advanced image processing also brings new challenges due to the high data dimensionality in both the spatial and spectral domains. To relieve this issue, in this paper, we present a novel multidomain subspace (MDS) feature representation and classification method for hyperspectral images. The proposed method is based on a patch alignment framework. In order to optimally combine the feature representations from the various domains and simultaneously enhance the subspace discriminability, we incorporate the supervised label information into each domain and further generalize the framework to a multidomain version. Furthermore, we develop an iterative approach to alternately optimize the MDS objective function by considering it as two subconvex optimizations. The classification performance on three standard hyperspectral remote sensing images confirms the superiority of the proposed MDS algorithm over the state-of-the-art subspace learning methods.
Keywords: Classification
Hyperspectral image (HSI)
Multidomain
Subspace learning
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on geoscience and remote sensing 
ISSN: 0196-2892
EISSN: 1558-0644
DOI: 10.1109/TGRS.2016.2582209
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