Context models for crf-based classification of multitemporal remote sensing data

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Hoberg, Thorsten; Rottensteiner, Franz; Heipke, Christian: Context models for crf-based classification of multitemporal remote sensing data. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-7 (2012), Nr. 1, S. 129-134. DOI: https://doi.org/10.5194/isprsannals-i-7-129-2012

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/5171

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Sum total of downloads: 44




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Abstract: 
The increasing availability of multitemporal satellite remote sensing data offers new potential for land cover analysis. By combining data acquired at different epochs it is possible both to improve the classification accuracy and to analyse land cover changes at a high frequency. A simultaneous classification of images from different epochs that is also capable of detecting changes is achieved by a new classification technique based on Conditional Random Fields (CRF). CRF provide a probabilistic classification framework including local spatial and temporal context. Although context is known to improve image analysis results, so far only little research was carried out on how to model it. Taking into account context is the main benefit of CRF in comparison to many other classification methods. Context can be already considered by the choice of features and in the design of the interaction potentials that model the dependencies of interacting sites in the CRF. In this paper, these aspects are more thoroughly investigated. The impact of the applied features on the classification result as well as different models for the spatial interaction potentials are evaluated and compared to the purely label-based Markov Random Field model.
License of this version: CC BY 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2012
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 18 40.91%
2 image of flag of United States United States 8 18.18%
3 image of flag of China China 6 13.64%
4 image of flag of Vietnam Vietnam 3 6.82%
5 image of flag of Nepal Nepal 2 4.55%
6 image of flag of Poland Poland 1 2.27%
7 image of flag of Netherlands Netherlands 1 2.27%
8 image of flag of Korea, Republic of Korea, Republic of 1 2.27%
9 image of flag of United Kingdom United Kingdom 1 2.27%
10 image of flag of Europe Europe 1 2.27%
    other countries 2 4.55%

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