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 language | English (US) |
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Title of host publication | Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 |
Editors | Joydeep Ghosh, Zoran Obradovic, Jennifer Dy, Zhi-Hua Zhou, Chandrika Kamath, Srinivasan Parthasarathy |
Publisher | Siam Society |
Pages | 650-658 |
Number of pages | 9 |
ISBN (Electronic) | 9781611972627 |
DOIs | |
State | Published - 2013 |
Event | SIAM International Conference on Data Mining, SDM 2013 - Austin, United States Duration: May 2 2013 → May 4 2013 |
Publication series
Name | Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 |
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Other
Other | SIAM International Conference on Data Mining, SDM 2013 |
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Country/Territory | United States |
City | Austin |
Period | 5/2/13 → 5/4/13 |
Bibliographical note
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