| dc.description.abstract | In this study, a novel feature line embedding based on support vector machine (SVM), termed SVMFLE, is proposed for dimension reduction (DR) and applied to improve the performance of generative adversarial networks (GAN) in hyperspectral image (HSI) classification. The GAN has successfully shown its powerful discriminative capability in many applications. However, since the traditional linear-based principle component analysis (PCA) pre-processing step in GAN cannot effectively obtain the nonlinear information, the feature line embedding based on support vector machine method, SVMFLE was proposed to alleviate this problem. The proposed SVMFLE dimension reduction scheme was performed through two stages. In the first SVM stage, the support vectors were extracted for obtaining a part of between-class scatters. Then in the second sensitivity analysis stage, the nearest neighbor (NN) classifier was used to find a suitable combination of the between-class scatters, and then obtain the final dimension reduced feature space. Since the obtained feature space was much more representative and discriminative than the conventional ones, the performance of GAN in HSI classification could be improved. The effectiveness of the proposed SVMFLE with GAN scheme was measured by comparing with the state-of-the-art work on three benchmark datasets. According to the experimental results: the performance of the proposed SVMFLE with GAN is better than the state-of-the-art, and got the accuracy of 96.26%, 86.61%, and 89.22% in Salinas, Indian Pines, and Pavia University datasets, respectively. | en_US |