An effective hyperspectral image retrieval method using integrated spectral and textural features

Zhenfeng Shao, Weixun Zhou, Qimin Cheng, Chunyuan Diao, Lei Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose - The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene. Design/methodology/approach - In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal components of the hyperspectral data based on Gabor filters. Then, the combination similarity between query image and other images in the database is calculated, and the first k more similar images are returned in descending order of the combination similarity. Findings - Some experiments are calculated using the airborne hyperspectral data of Washington DC Mall. According to the experimental results, the proposed method improves the retrieval results, especially for image categories that have regular textural structures. Originality/value - The paper presents an effective retrieval method for hyperspectral images.

Original languageEnglish (US)
Pages (from-to)274-281
Number of pages8
JournalSensor Review
Volume35
Issue number3
DOIs
StatePublished - Jun 15 2015
Externally publishedYes

Keywords

  • Image processing
  • Spectral analysis

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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