Classification of settlement areas in remote sensing imagery using conditional random fields

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Hoberg, T.; Rottensteiner, F.: Classification of settlement areas in remote sensing imagery using conditional random fields. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [100 Years ISPRS Advancing Remote Sensing Science, Pt 1] 38 (2010), Nr. 7A, S. 53-58

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/1114

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Sum total of downloads: 59




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Abstract: 
Land cover classification plays a key role for various geo-based applications. Numerous approaches for the classification of settlements in remote sensing imagery have been developed. Most of them assume the features of neighbouring image sites to be conditionally independent. Using spatial context information may enhance classification accuracy, because dependencies of neighbouring areas are taken into account. Conditional Random Fields (CRF) have become popular in the field of pattern recognition for incorporating contextual information because of their ability to model dependencies not only between the class labels of neighbouring image sites, but also between the labels and the image features. In this work we investigate the potential of CRF for the classification of settlements in high resolution satellite imagery. To highlight the power of CRF, tests were carried out using only a minimum set of features and a simple model of context. Experiments were performed on an Ikonos scene of a rural area in Germany. In our experiments, completeness and correctness values of 90% and better could be achieved, the CRF approach was clearly outperforming a standard Maximum-Likelihood-classification based on the same set of features.
License of this version: CC BY 3.0
Document Type: article
Publishing status: publishedVersion
Issue Date: 2010
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 35 59.32%
2 image of flag of France France 5 8.47%
3 image of flag of China China 4 6.78%
4 image of flag of United States United States 3 5.08%
5 image of flag of Ghana Ghana 3 5.08%
6 image of flag of United Kingdom United Kingdom 2 3.39%
7 image of flag of Canada Canada 2 3.39%
8 image of flag of Ukraine Ukraine 1 1.69%
9 image of flag of Mexico Mexico 1 1.69%
10 image of flag of India India 1 1.69%
    other countries 2 3.39%

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