Abstract
In this paper, a novel feature line embedding (FLE) algorithm based on support vector machine (SVM), referred to as SVMFLE, is proposed for dimension reduction (DR) and for improving the performance of the generative adversarial network (GAN) in hyperspectral image (HSI) classification. The GAN has successfully shown high discriminative capability in many applications. However, owing to the traditional linear-based principal component analysis (PCA) the pre-processing step in the GAN cannot effectively obtain nonlinear information; to overcome this problem, feature line embedding based on support vector machine (SVMFLE) was proposed. The proposed SVMFLE DR scheme is implemented through two stages. In the first scatter matrix calculation stage, FLE within-class scatter matrix, FLE between-scatter matrix, and support vector-based FLE between-class scatter matrix are obtained. Then in the second weight determination stage, the training sample dispersion indices versus the weight of SVM-based FLE between-class matrix are calculated to determine the best weight between-scatter matrices and obtain the final transformation matrix. Since the reduced feature space obtained by the SVMFLE scheme is much more representative and discriminative than that obtained using conventional schemes, the performance of the GAN in HSI classification is higher. The effectiveness of the proposed SVMFLE scheme with GAN or nearest neighbor (NN) classifiers was evaluated by comparing them with state-of-the-art methods and using three benchmark datasets. According to the experimental results, the performance of the proposed SVMFLE scheme with GAN or NN classifiers was higher than that of the state-of-the-art schemes in three performance indices. Accuracies of 96.3%, 89.2%, and 87.0% were obtained for the Salinas, Pavia University, and Indian Pines Site datasets, respectively. Similarly, this scheme with the NN classifier also achieves 89.8%, 86.0%, and 76.2% accuracy rates for these three datasets.
Highlights
Remote Sens. 2021, 13, 130 of the large number of hyper spectral image (HSI) pixels, numerous schemes applied in HSI classification were based on supervised learning [1], including the support vector machine (SVM) [2,3], nearest feature line [4,5], random forest [6], manifold learning [7,8], sparse representation [9], and deep learning (DL) [1]
This section, experiments conducted forperformance the performance evaluation. In this somesome experiments werewere conducted for the evaluation of of the proposed benchmark datasets, the proposed SVMFLE dimension reduction (DR) algorithm in HSI classification
In this sub-section, the Indian Pines Site (IPS) dataset was used to evaluate the effectiveness of the proposed method and compared with the state-of-the-art methods
Summary
Zhang et al [19] employed a sparse low-rank approximation scheme to regularize a manifold structure, and HSI data were treated as cube data for classification These methods based on manifold learning all preserve the topology of the locality of training samples and outperform the traditional linear measurement methods. The proposed idea was a simple and effective idea for multiple kernel learning, it used kernel discriminant analysis based on linear measurement for classification, and would not preserve the manifold topology of multiple kernels in high dimension space. Resembling the idea of multiple kernel learning, Zhang et al [27] presented an algorithm for multiple-feature integration based on multiple kernel learning and employed it to classify HSI data; their proposed algorithm assembles shape, texture, and spectral information to improve the performance of HSI classification.
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