Conditional Random Fields for Land Use/Land Cover Classification and Complex Region Detection

Date

2012-11-09

Author

Can, Gulcan
Firat, Orhan
Yarman Vural, Fatoş Tunay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Developing a complex region detection algorithm that is aware of its contextual relations with several classes necessitates statistical frameworks that can encode contextual relations rather than simple rule-based applications or heuristics. In this study, we present a conditional random field (CRF) model that is generated over the results of a robust local discriminative classifier in order to reveal contextual relations of complex objects and land use/land cover (LULC) classes. The proposed CRF model encodes the contextual relation between the LULC classes and complex regions (airfields) as well as updates labels of the discriminative classifier and labels the complex region in a unified framework. The significance of the developed model is that it does not need any explicit parameters and/or thresholds along with heuristics or expert rules.

Subject Keywords

Conditional random fields, Land use/land cover, Complex region de-tection, Satellite imagery

URI

https://hdl.handle.net/11511/53546

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

G. Can, O. Firat, and F. T. Yarman Vural, “Conditional Random Fields for Land Use/Land Cover Classification and Complex Region Detection,” 2012, vol. 7626, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53546.