Using label noise robust logistic regression for automated updating of topographic geospatial databases

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Maas, A.; Rottensteiner, F.; Heipke, C.: Using label noise robust logistic regression for automated updating of topographic geospatial databases. In: XXIII ISPRS Congress, Commission VII 3 (2016), Nr. 7, S. 133-140. DOI:

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Sum total of downloads: 313

Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.
License of this version: CC BY 3.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2016
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 120 38.34%
2 image of flag of United States United States 41 13.10%
3 image of flag of China China 34 10.86%
4 image of flag of Hong Kong Hong Kong 10 3.19%
5 image of flag of Korea, Republic of Korea, Republic of 8 2.56%
6 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 8 2.56%
7 image of flag of France France 8 2.56%
8 image of flag of Belgium Belgium 8 2.56%
9 image of flag of India India 7 2.24%
10 image of flag of Canada Canada 7 2.24%
    other countries 62 19.81%

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