Assessment of different classification algorithms for burnt land discrimination

In this paper, satellite-based remote sensing techniques are used for assessing the damage after a forest fire. Here, burnt land mapping is based on a single after-fire satellite image (SPOT 5). Both support vector machines (SVM) and traditional classification algorithms such as the K-nearest neighbours or the K-means are used to discriminate burnt from unburnt areas. An automatic method combining K-means and SVM is presented and its performances are compared to more classical methods. Maps produced by the different classifiers are also compared to official ground truth provided by the French Space Agency (CNES).