Combining rotation forest and multi-scale segmentation for the classification of hyperspectral data
In order to tackle the main issues (high-precision classifier and spatial information model) in the classification of hyperspectral images, we proposed a new approach that combines two powerful techniques: Rotation Forest and multi-scale segmentation. Multi-scale segmentation was used to model the spatial information for different classes. Then, the objects produced by multi-scale segmentation was treated as the input of the Rotation Forest classifier. Furthermore, we combined the classification results generated by Rotation Forest and multi-scale segmentation to generate the final result. Experimental results demonstrate the excellent performances of this proposed approach.