We propose an algorithm for very high-resolution
satellite image classification that combines
non-supervised segmentation with a supervised
classification. Both multi-spectral data and local
spatial priors are used in the Gaussian Hidden Markov
Random Field (GHMRF) model for the segmentation. Then,
two classifiers, Mahalanobis distance classifier and SVM,
are studied using intensity, texture and shape features.
Validation is done qualitatively and quantitatively by
comparison with a manual classification used as a ground
truth. Results show very good performance of our approach
in comparison to existing techniques. Also, we
demonstrate that spectral and spatial features calculated
on segmented regions are much more discriminant than the
spectral features of the pixels taken individually for
the classification task.