Gabor phase feature-based hyperspectral imagery classification

Sen Jia, Huimin Xie, Lin Deng, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


In this paper, a three-dimensional (3D) Gabor phase coding and Hamming distance matching approach, called 3DGPC-HDM, is proposed for hyperspectral imagery classification. Specifically, the Gabor phase features with certain orientations are utilized, which are then encoded by a simple quadrant bit coding scheme. Next, a normalized Hamming distance matching method has been introduced to determine the similarity of two samples, and the nearest neighbor classifier is routinely used for recognition. The extensive experiments on two real hyper-spectral data sets have demonstrated superior performance of the proposed 3DGPC-HDM approach over the state-of-the-art methods in the literature.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781509060672
Publication statusPublished - 28 Aug 2017
Externally publishedYes
Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
Duration: 10 Jul 201714 Jul 2017

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2017 IEEE International Conference on Multimedia and Expo, ICME 2017
Country/TerritoryHong Kong
CityHong Kong


  • Gabor phase
  • Hyperspectral imagery

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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