Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data

Z. Zhang, M. Y. Yang, M. Zhoua

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote sensing applications, such as object classification and recognition. In this paper, we introduce a novel multi-source hierarchical conditional random field (MSHCRF) model to fuse features extracted from remote sensing images and LiDAR data for image classification. Firstly, typical features are selected to obtain the interest regions from multi-source data, then MSHCRF model is constructed to exploit up the features, category compatibility of images and the category consistency of multi-source data based on the regions, and the outputs of the model represents the optimal results of the image classification. Competitive results demonstrate the precision and robustness of the proposed method.

Original languageEnglish
Title of host publicationISPRS Hannover Workshop 2013 (Volume XL-1/W1)
Subtitle of host publicationWG I/4, III/4, IC IV/VIII, VII/2
EditorsC. Heipke, K. Jacobsen, F. Rottensteiner, U. Sörgel
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages389-392
Number of pages4
VolumeXL
Edition1W1
DOIs
Publication statusPublished - 1 Jan 2013
EventISPRS Hannover Workshop 2013 - Hannover, Germany
Duration: 21 May 201324 May 2013

Publication series

NameInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
PublisherCopernicus
ISSN (Print)1682-1750

Conference

ConferenceISPRS Hannover Workshop 2013
CountryGermany
CityHannover
Period21/05/1324/05/13

Fingerprint

Remote sensing
Fusion reactions
Image classification
remote sensing
image classification
Electric fuses
complementarity

Keywords

  • Conditional random field
  • Feature fusion
  • Hierarchical model
  • Image classification
  • Multi-source data
  • ITC-GOLD

Cite this

Zhang, Z., Yang, M. Y., & Zhoua, M. (2013). Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data. In C. Heipke, K. Jacobsen, F. Rottensteiner, & U. Sörgel (Eds.), ISPRS Hannover Workshop 2013 (Volume XL-1/W1): WG I/4, III/4, IC IV/VIII, VII/2 (1W1 ed., Vol. XL, pp. 389-392). (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives). International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprsarchives-XL-1-W1-389-2013
Zhang, Z. ; Yang, M. Y. ; Zhoua, M. / Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data. ISPRS Hannover Workshop 2013 (Volume XL-1/W1): WG I/4, III/4, IC IV/VIII, VII/2. editor / C. Heipke ; K. Jacobsen ; F. Rottensteiner ; U. Sörgel. Vol. XL 1W1. ed. International Society for Photogrammetry and Remote Sensing (ISPRS), 2013. pp. 389-392 (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives).
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abstract = "Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote sensing applications, such as object classification and recognition. In this paper, we introduce a novel multi-source hierarchical conditional random field (MSHCRF) model to fuse features extracted from remote sensing images and LiDAR data for image classification. Firstly, typical features are selected to obtain the interest regions from multi-source data, then MSHCRF model is constructed to exploit up the features, category compatibility of images and the category consistency of multi-source data based on the regions, and the outputs of the model represents the optimal results of the image classification. Competitive results demonstrate the precision and robustness of the proposed method.",
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Zhang, Z, Yang, MY & Zhoua, M 2013, Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data. in C Heipke, K Jacobsen, F Rottensteiner & U Sörgel (eds), ISPRS Hannover Workshop 2013 (Volume XL-1/W1): WG I/4, III/4, IC IV/VIII, VII/2. 1W1 edn, vol. XL, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing (ISPRS), pp. 389-392, ISPRS Hannover Workshop 2013, Hannover, Germany, 21/05/13. https://doi.org/10.5194/isprsarchives-XL-1-W1-389-2013

Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data. / Zhang, Z.; Yang, M. Y.; Zhoua, M.

ISPRS Hannover Workshop 2013 (Volume XL-1/W1): WG I/4, III/4, IC IV/VIII, VII/2. ed. / C. Heipke; K. Jacobsen; F. Rottensteiner; U. Sörgel. Vol. XL 1W1. ed. International Society for Photogrammetry and Remote Sensing (ISPRS), 2013. p. 389-392 (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Zhang Z, Yang MY, Zhoua M. Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data. In Heipke C, Jacobsen K, Rottensteiner F, Sörgel U, editors, ISPRS Hannover Workshop 2013 (Volume XL-1/W1): WG I/4, III/4, IC IV/VIII, VII/2. 1W1 ed. Vol. XL. International Society for Photogrammetry and Remote Sensing (ISPRS). 2013. p. 389-392. (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives). https://doi.org/10.5194/isprsarchives-XL-1-W1-389-2013