Building change detection by histogram classification

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

Abstract

This paper presents a supervised classification method applied to building change detection in VHR aerial images. Multi-spectral stereo pairs of 0.3m resolution have been processed to derive elevation, vegetation index and colour features. These features help filling a 5-dimensional histogram whose bins finally hold the ratio of built-up and non built-up pixels, according to the vector database to be updated. This ratio is used as building confidence at each pixel to issue a building confidence map from which to perform building verification and detection. The implementation based on histogram is very simple to code, very fast in execution and compares in this application to a state-of-the-art supervised classifier. It has been tested for the Belgian National Mapping Agency (IGN) to identify areas with high probability of change in building layers.

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2011
Pages409-415
Number of pages7
DOIs
Publication statusPublished - 2011
Event7th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2011 - Dijon, France
Duration: 28 Nov 20111 Dec 2011

Publication series

NameProceedings - 7th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2011

Conference

Conference7th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2011
Country/TerritoryFrance
CityDijon
Period28/11/111/12/11

Keywords

  • Building change detection
  • Digital Surface Model
  • Histogram classification
  • Multi-spectral images

Fingerprint

Dive into the research topics of 'Building change detection by histogram classification'. Together they form a unique fingerprint.

Cite this