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

International audienceMapping 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