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Generalized Tensor Regression for Hyperspectral Image Classification
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DOI：10.1109/TGRS.2019.2944989.png)
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Abstract
En 中文
In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, a simple and effective classifier, i.e., the ridge regression for multivariate labels, is extended to its tensorial version by taking advantages of tensorial representation. Then, the discrimination information of different modes is exploited to further strengthen the capacity of the model. Moreover, the model can be simplified and solved easily. Different from traditional tensorial methods, the proposed model can be utilized to capture not only the intrinsic structure of data in a physical sense but also the generalized relationship of data in a logical sense. Our proposed approach is shown to be effective for different classification purposes on a series of instantiations. Specifically, our experiment results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer, the reflective optics spectrographic imaging system and the ITRES CASI-1500 demonstrate the effectiveness of the proposed approach as compared to other tensor-based classifiers and multiple kernel learning methods.
Keywords:
Hyperspectral imaging
Imaging
Training
Urban areas
Sun
Column generation (CG)
hyperspectral image classification
multiple kernel
feature learning
tensor decomposition
tensor regression
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