In this article, the task of remote-sensing image classification is tackled with local maximal margin approaches. First, we introduce a set of local kernel-based classifiers that alleviate the computational limitations of local support vector machines (SVMs), maintaining at the same time high classification accuracies. Such methods rely on the following idea: (a) during training, build a set of local models covering the considered data and (b) during prediction, choose the most appropriate local model for each sample to evaluate. Additionally, we present a family of operators on kernels aiming to integrate the local information into existing (input) kernels in order to obtain a quasi-local (QL) kernel. To compare the performances achieved by the different local approaches, an experimental analysis was conducted on three distinct remote-sensing data sets. The obtained results show that interesting performances can be achieved in terms of both classification accuracy and computational cost. © 2012 Copyright Taylor and Francis Group, LLC.
CITATION STYLE
Segata, N., Pasolli, E., Melgani, F., & Blanzieri, E. (2012). Local SVM approaches for fast and accurate classification of remote-sensing images. International Journal of Remote Sensing, 33(19), 6186–6201. https://doi.org/10.1080/01431161.2012.678947
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