Support vector machines for land usage classification in Landsat TM imagery
Land usage classification is an essential part of many remote sensing applications for mapping, inventory, and yield estimation. In this contribution, we evaluate the potential of the support vector machines for remote sensing applications. Moreover, we expand this discriminative technique by a novel Bayesian approach to estimate the confidence of each classification. These estimates are combined with a priori knowledge about topological relations of class labels using a contextual classification step based on the iterative conditional mode algorithm (ICM). As shown for Landsat TM imagery, this strategy is highly competitive and outperforms several commonly used classification schemes.