Marginalized kernel-based feature fusion method for VHR object classification
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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 approach is effective in various feature combination, and has outperformed the SVM baseline method.
2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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