| Popis: |
Band selection methods often suppose normal distribution or, at least, a significant number of samples per class to compute statistical parameters. In this paper, we propose a band selection technique that needs very few training samples per class to be effective. To take into account the spectral variability inside the classes and to be independent on statistic parameters, we propose to use a criterion which measures the separability between two classes. This criterion is based on the extension of Spectral Angle Mapper (SAM) to a SAM within- and between-classes. The proposed selection method consists in eliminating spectral bands from the original set, thanks to the previous criterion that check the increase of separability measure between classes on the remaining bands subset. We use a stochastic algorithm to choose, at each step, which band to eliminate. We proceed by successive elimination until we reach the number of desired bands or the maximum of the criterion. This top-down method allows taking simultaneously into account all the interesting bands during the whole process, instead of selecting them one by one. The method provides, at the end, the selected spectral bands for a pair of classes. We expand this two-class selection technique to the multiclass band selection. We improve the method by adding a pre-selection of interesting bands considering a measure of a spectral signal on noise ratio. Some examples are given to show the effectiveness of the method: as one main application is classification, we compare the results of classification achieved after the data reduction made by different methods. We check the efficiency according to the number of training samples. |
