Multi-source multi-scale hierarchical conditional random field model for remote sensing image classification

Z. Zhang*, M. Y. Yang, M. Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

2 Citations (Scopus)
2 Downloads (Pure)

Abstract

Fusion of remote sensing images and LiDAR data provides complimentary information for the remote sensing applications, such as object classification and recognition. In this paper, we propose a novel multi-source multi-scale hierarchical conditional random field (MSMSH-CRF) model to integrate features extracted from remote sensing images and LiDAR point cloud data for image classification. MSMSH-CRF model is then constructed to exploit the features, category compatibility of multi-scale images and the category consistency of multi-source data based on the regions. The output of the model represents the optimal results of the image classification. We have evaluated the precision and robustness of the proposed method on airborne data, which shows that the proposed method outperforms standard CRF method.

Original languageEnglish
Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages293-300
Number of pages8
Volume2
Edition3W4
DOIs
Publication statusPublished - 12 Mar 2015
Externally publishedYes
EventJoint ISPRS workshops on Photogrammetric Image Analysis, PIA 2015 and High Resolution Earth Imaging for Geospatial Information, HRIGI 2015 - Munich, Germany
Duration: 25 Mar 201527 Mar 2015

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus
ISSN (Print)2194-9042

Conference

ConferenceJoint ISPRS workshops on Photogrammetric Image Analysis, PIA 2015 and High Resolution Earth Imaging for Geospatial Information, HRIGI 2015
Abbreviated titlePIA 2015
CountryGermany
CityMunich
Period25/03/1527/03/15

Keywords

  • Classification
  • Fusion
  • Hierarchical
  • LIDAR
  • Multisensor
  • Performance
  • Vision
  • ITC-GOLD

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