Combining Rotation Forest and Multiscale Segmentation for the Classification of Hyperspectral Data
In order to tackle two key issues, e.g., high-accuracy classifier and spatial information model, in the classification of hyperspectral images, a new classification scheme, which combines two powerful techniques, i.e., rotation forest (RoF) and multiscale (MS) segmentation, is proposed in this paper. MS segmentation is used to obtain the spatial information from different levels. Then, the objects produced by MS segmentation are treated as the input of the RoF classifier. Furthermore, multiple classification results generated by the RoF classifiers and MS segmentation are combined using a majority voting rule to generate the final result. Experimental results on two real hyperspectral datasets demonstrate that the proposed method performs particularly well in terms of overall and class-specific accuracies and generates the classification map with much more homogeneous regions than traditional methods. Moreover, the impacts of parameters on the classification performances are also analyzed.