Change detection from temporal sequences of class labels: Application to land cover change mapping

Varun Mithal, Ankush Kharidelwal, Shyam Boriah, Karsten Steinhaeuser, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mapping land cover change is an important problem for the scientific community as well as policy makers. Traditionally, bi-temporal classification of satellite data is used to identify areas of land cover change. However, these classification products often have errors due to classifier inaccuracy or poor data, which poses significant issues when using them for land cover change detection. In this paper, we propose a generative model for land cover label sequences and use it to reassign a more accurate sequence of land cover labels to every pixel. Empirical evaluation on real and synthetic data suggests that the proposed approach is effective in capturing the characteristics of land cover classification and change processes, and produces significantly improved classification and change detection products.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2013, SMD 2013
PublisherSociety for Industrial and Applied Mathematics Publications
Pages650-658
Number of pages9
ISBN (Electronic)9781627487245
StatePublished - Jan 1 2013
Event13th SIAM International Conference on Data Mining, SMD 2013 - Austin, United States
Duration: May 2 2013May 4 2013

Publication series

NameSIAM International Conference on Data Mining 2013, SMD 2013

Other

Other13th SIAM International Conference on Data Mining, SMD 2013
CountryUnited States
CityAustin
Period5/2/135/4/13

Fingerprint Dive into the research topics of 'Change detection from temporal sequences of class labels: Application to land cover change mapping'. Together they form a unique fingerprint.

Cite this