Generalized Tensor Regression for Hyperspectral Image Classification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Jianjun Liu
  • Zebin Wu
  • Liang Xiao
  • Jun Sun
  • Hong Yan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8877994
Pages (from-to)1244-1258
Journal / PublicationIEEE Transactions on Geoscience and Remote Sensing
Volume58
Issue number2
Online published21 Oct 2019
Publication statusPublished - Feb 2020

Abstract

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.

Research Area(s)

  • Column generation (CG), hyperspectral image classification, multiple kernel/feature learning, tensor decomposition, tensor regression

Citation Format(s)

Generalized Tensor Regression for Hyperspectral Image Classification. / Liu, Jianjun; Wu, Zebin; Xiao, Liang; Sun, Jun; Yan, Hong.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 58, No. 2, 8877994, 02.2020, p. 1244-1258.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review