Transfer learning based on logistic regression

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Paul, A.; Rottensteiner, F.; Heipke, C.: Transfer learning based on logistic regression. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40 (2015), Nr. 3W3, S. 145-152. DOI:

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Sum total of downloads: 163

In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added to the current training set which, at the beginning, only contains source features and, simultaneously, a number of source features are deleted from the current training set. Experimental results based on a test series with synthetic and real data constitutes a first proof-of-concept of the proposed method.
License of this version: CC BY 3.0
Document Type: article
Publishing status: publishedVersion
Issue Date: 2015
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 82 50.31%
2 image of flag of United States United States 16 9.82%
3 image of flag of China China 9 5.52%
4 image of flag of Taiwan Taiwan 7 4.29%
5 image of flag of India India 6 3.68%
6 image of flag of United Kingdom United Kingdom 5 3.07%
7 image of flag of Japan Japan 4 2.45%
8 image of flag of Canada Canada 4 2.45%
9 image of flag of Brazil Brazil 4 2.45%
10 image of flag of Netherlands Netherlands 3 1.84%
    other countries 23 14.11%

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