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Including invariances in SVM remote sensing image classification

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4 Author(s)
Emma Izquierdo-Verdiguier ; Image Processing Laboratory (IPL). Universitat de València, Spain ; Valero Laparra ; Luis Gómez-Chova ; Gustavo Camps-Valls

This paper introduces a simple method to include invariances in support vector machine (SVM) for remote sensing image classification. We rely on the concept of virtual support vectors, by which the SVM is trained with both the selected support vectors and synthetic examples encoding the invariance of interest. The algorithm is very simple and effective, as demonstrated in two particularly interesting examples: invariance to the presence of shadows and to rotations in patchbased image segmentation. The improved accuracy (around +6% both in OA and Cohen's κ statistic), along with the simplicity of the approach encourage its use and extension to encode other invariances and other remote sensing data analysis applications.

Published in:

2012 IEEE International Geoscience and Remote Sensing Symposium

Date of Conference:

22-27 July 2012