Change Detection Based on Gabor Wavelet Features for Very High Resolution Remote Sensing Images

Zhenxuan Li, Wen Zhong Shi, Hua Zhang, Ming Hao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)

Abstract

In this letter, we propose a change detection method based on Gabor wavelet features for very high resolution (VHR) remote sensing images. First, Gabor wavelet features are extracted from two temporal VHR images to obtain spatial and contextual information. Then, the Gabor-wavelet-based difference measure (GWDM) is designed to generate the difference image. In GWDM, a new local similarity measure is defined, in which the Markov random field neighborhood system is incorporated to obtain a local relationship, and the coefficient of variation method is applied to discriminate contributions from different features. Finally, the fuzzy c-means cluster algorithm is employed to obtain the final change map. Experiments employing QuickBird and SPOT5 images demonstrate the effectiveness of the proposed approach.
Original languageEnglish
Article number7888966
Pages (from-to)783-787
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume14
Issue number5
DOIs
Publication statusPublished - 1 May 2017

Keywords

  • Change detection
  • coefficient of variation
  • fuzzy c-means (FCM)
  • Gabor wavelet
  • Markov random field (MRF)
  • remote sensing
  • very high resolution (VHR)

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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