Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns

Cai-ling Wang, Jinchang Ren, Hong-wei Wang, Yinyong Zhang, Jia Wen

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
29 Downloads (Pure)


It is of great interest in spectral-spatial features classification for hyperspectral images (HSI) with high spatial resolution. This paper presents a novel Spectral-spatial classification method for improving hyperspectral image classification accuracy. Specifically, a new texture feature extraction algorithm exploits spatial texture feature from spectrum is proposed. It employs local binary patterns (LBPs) in order to extract the image texture feature with respect to spectrum information diversity (SID) to measure the differences of spectrum information. The classifier adopted in this work is support vector machine (SVM) because of its outstanding classification performances. In this paper, two real hyperspectral image datasets are used for testing the performance of the proposed method. Our experimental results from real hyperspectral images indicate that the proposed framework can enhance the classification accuracy compare to traditional alternatives.
Original languageEnglish
Pages (from-to)29889-29903
Number of pages15
JournalMultimedia Tools and Applications
Issue number22
Early online date11 Apr 2018
Publication statusPublished - 30 Nov 2018


  • hyperspectral image classification
  • spectral-spatial analysis
  • local binary patterns
  • spectrum information diversity
  • support vector machine


Dive into the research topics of 'Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns'. Together they form a unique fingerprint.

Cite this