Kernel-based Framework for Multi-Temporal and Multi-Source Remote Sensing Data Classification and Change Detection (2007)
| Citations: | 5 - 0 self |
BibTeX
@MISC{Camps-valls07kernel-basedframework,
author = {Gustavo Camps-valls and Senior Member and Luis Gómez-chova and Jose Luis Rojo-álvarez and Manel Martínez-ramón and Senior Member},
title = {Kernel-based Framework for Multi-Temporal and Multi-Source Remote Sensing Data Classification and Change Detection},
year = {2007}
}
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Abstract
Multi-temporal classification of remote sensing images is a challenging problem, in which efficient combination of different sources of information (e.g. temporal, contextual, or multi-sensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multi-temporal classification of remote sensing images is presented. The second contribution is the development of non-linear kernel classifiers for the well-known difference and ratioing change detection methods, by formulating them in an adequate high dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multi-sensor images with different levels of non-linear sophistication. The binary support vector classifier (SVC) and the one-class support vector domain
