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A bag-of-visual words approach based on optimal segmentation scale for high resolution remote sensing image classification

  • School of Electronics and Information Engineering, Harbin Institute of Technology

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

Abstract

High resolution remote sensing imagery can provide more useful information, such as spectral, shape and texture information. However, traditional pixel-based image classification approaches may suffer the increase of within-class spectral variation with improved spatial resolution. This paper presents a novel method which combines the optimal segmentation scale with Bag-of-Visual Words (BOV) representation for object-oriented classification. More precisely, an improved estimation of scale parameter (ESP) tool is adopted to determine the optimal parameters in multi-scale image segmentation. BOV is introduced to construct the midlevel representations instead of low-level features for object description. Then Support vector machine (SVM) is used for classification. And the experiments are conducted on high spatial resolution images to validate the proposed algorithm.

Original languageEnglish
Title of host publication2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1012-1015
Number of pages4
ISBN (Electronic)9781479979295
DOIs
StatePublished - 10 Nov 2015
Externally publishedYes
Event2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy
Duration: 26 Jul 201531 Jul 2015

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2015-November
ISSN (Print)2153-6996
ISSN (Electronic)2153-7003

Conference

Conference2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Country/TerritoryItaly
CityMilan
Period26/07/1531/07/15

Keywords

  • Bag-of-Visual Words (BOV)
  • midlevel representation
  • multi-scale segmentation
  • object-oriented classification

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