Gabor Cube Selection Based Multitask Joint Sparse Representation for Hyperspectral Image Classification

Sen Jia, Jie Hu, Yao Xie, Linlin Shen, Xiuping Jia, Qingquan Li

Research output: Journal PublicationArticlepeer-review

80 Citations (Scopus)


The large amount of spectral and spatial information contained in hyperspectral imagery has provided great opportunity to effectively characterize and identify the surface materials of interest. As a novel feature extraction technique, a series of Gabor wavelet filters with different scales and frequencies was applied on hyperspectral data to extract spectral-spatial-combined features, which produced impressive performance on pixel-oriented classification. However, the incredibly large number of Gabor features could cause too much burden for onboard computation, limiting the efficiency of the method. To make matters worse, due to the nonhomogeneous spatial distribution of materials as well as the different characteristics of the constructed Gabor filters, some Gabor features could have a smaller or even negative impact on material representation, deteriorating the classification accuracy eventually. In this paper, a Gabor cube selection based multitask joint sparse representation approach, abbreviated as GS-MTJSRC, was proposed for hyperspectral image classification. First, based on the Fisher discrimination criterion, the most representative Gabor cubes for each class were picked out. Next, under multitask joint sparse representation framework, a coefficient vector could be obtained for each test sample with the selected Gabor cube features, which could be directly used for the following residual-based classification. Experimental results on three real hyperspectral data sets with different characteristics and spatial resolutions demonstrated the feasibility and efficiency of the proposed method.

Original languageEnglish
Article number7389391
Pages (from-to)3174-3187
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number6
Publication statusPublished - Jun 2016
Externally publishedYes


  • Fisher discrimination criterion
  • Gabor features
  • hyperspectral image classification
  • multitask sparse representation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences (all)


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