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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...
Due to the imbalance in obtaining labeled samples for different land-cover classes, hyperspectral image classification encounters the issue of imbalanced classification. In this paper, a novel and effective method is proposed to address the imbalanced learning problem in hyperspectral image classification, which combines support vector machine (SVM) and sampling strategy. The main novelty and contribution...
This paper proposes to improve classification accuracy of hyperspectral images by using sample interpolation when limited training samples are available. The training data size is artificially increased by adding training samples that have been interpolated from the original training data. Two approaches are presented with different number of training patterns being considered in the interpolation...
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