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Hyperspectral image classification from multiscale description with constrained connectivity and metric learning

Sébastien Lefèvre 1 Laëtitia Chapel 1 François Merciol 1
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Mapping of remote sensing data is usually done through image classification. For hyperspectral images, the classification process often relies only on the spectral signature of each single pixel. Nevertheless, combining spatial and spectral features has been a promising way for accuracy improvement. We address here this problem by computing spectral features from spatially identified regions, sampled from a hierarchical image representation, namely α-tree, built with prior knowledge. The sampling of the tree nodes (i.e., regions) is based on the paradigm of constrained connectivity and the global range criterion. In this paper, we extend this criterion to hy-perspectral data and apply it to our knowledge-based α-tree. Our results show an improvement of pixelwise classification accuracy over spectral features only.
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https://hal.archives-ouvertes.fr/hal-00998254
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Sébastien Lefèvre, Laëtitia Chapel, François Merciol. Hyperspectral image classification from multiscale description with constrained connectivity and metric learning. 6th International Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2014), Jun 2014, Lausanne, Switzerland. ⟨hal-00998254⟩

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