Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection

Camps-Valls, Gustavo

doi:10.1109/TGRS.2008.916201

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Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection

2008-06-01

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Camps-Valls, Gustavo *

Luis Gómez‐Chova

Jordi Muñoz-Marı́

José Luis Rojo‐Álvarez

Manel Martínez‐Ramón

DOI：10.1109/TGRS.2008.916201

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Abstract

En 中文

The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) 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 multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear 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 multisensor images with different levels of nonlinear sophistication. The binary support vector (SV) classifier and the one-class SV domain description classifier are evaluated by using both linear and nonlinear kernel functions. Good performance on synthetic and real multitemporal classification scenarios illustrates the generalization of the framework and the capabilities of the proposed algorithms.

Keywords:

terms-change detection

information fusion

kernel methods

multisource

multitemporal classification

support vector (SV) domain description (SVDD)

support vector machine (SVM)