Marginalized kernel-based feature fusion method for VHR object classification

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Author(s)
Liu, Chuntian
Wei, Wei
Bai, Xiao
Zhou, Jun
Griffith University Author(s)
Year published
2013
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Many image features can be extracted from very high resolution remote sensing images for object classification. Proper feature combination is a step towards better classification performance. In this paper, we propose a logistic regressionbased feature fusion method which assigns different weights to different features. This method considers the probability that two images belongs to the same classes and the imageto- class similarity to define the similarity between two objects. This similarity is used as a marginalized kernel for the final classifier construction. Experiments on remote sensing images suggest that this ...
View more >Many image features can be extracted from very high resolution remote sensing images for object classification. Proper feature combination is a step towards better classification performance. In this paper, we propose a logistic regressionbased feature fusion method which assigns different weights to different features. This method considers the probability that two images belongs to the same classes and the imageto- class similarity to define the similarity between two objects. This similarity is used as a marginalized kernel for the final classifier construction. Experiments on remote sensing images suggest that this approach is effective in various feature combination, and has outperformed the SVM baseline method.
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View more >Many image features can be extracted from very high resolution remote sensing images for object classification. Proper feature combination is a step towards better classification performance. In this paper, we propose a logistic regressionbased feature fusion method which assigns different weights to different features. This method considers the probability that two images belongs to the same classes and the imageto- class similarity to define the similarity between two objects. This similarity is used as a marginalized kernel for the final classifier construction. Experiments on remote sensing images suggest that this approach is effective in various feature combination, and has outperformed the SVM baseline method.
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Conference Title
2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
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Subject
Computer vision