Marginalized kernel-based feature fusion method for VHR object classification

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 regression-based 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 image-to-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.