Rotation-based object-oriented ensemble in land use land cover classification of hyperspectral data

To classify hyperspectral images by using different techniques many classifiers have been produce better performance for object-oriented classification of hyperspectral remote sensing images. To improve the classification accuracy, first time we investigated an ensemble principle named Rotation-Based object-oriented classification of hyperspectral images (RoBOO). It is the combination of segmentation with support vector machine and nearest neighborhood algorithm. It uses random features selection and data transformation (PCA), technique to improve accuracy and diversity. In this paper we explained our new classification strategy which combines the multi-resolution segmentation (MRS) with SVM, NN classifier. MRS is used to obtain the objects by tuning the parameters included compactness, scale and shape. Furthermore, multiple classification results are produced by proposed method and results were integrated by using majority voting rule to release final results. Mostly used data transformation technique PCA was used. An experiential study on one hyperspectral dataset indicates that the suggested RoBOO slightly surpasses the single SVM and NN. The rotation-matrix with the MRS improved the performance. The effect of parameters on accuracy of RoBOO (different training sets, amount of features in each subset, compactness, scale parameters, shape) is examined as well in this document. We deduced that the integrations of SVM and NN with MRS were impressive in the object-oriented hyperspectral classification of land cover land use.