关键词: 高光谱图像, 半监督全局判别分析, 半监督局部稀疏保持, 特征提取, 空间相关性

Abstract: In view of the problem of “dimension disaster” in hyperspectral images, this paper proposes a Global discriminant and Local Sparse preserving Semi-supervised Feature Extraction algorithm（GLSSFE）. The algorithm exploits the divergence matrix of LDA algorithm to preserve the global intra-class discriminant information and the global inter-class discriminant information of the labeled data. It utilizes semi-supervised PCA to preserve global structure of the labeled data and the unlabeled data. It uses sparse representation optimization model to find the nonlinear structure of data adaptively. Local discriminant weight of intra-class and local discriminant weight of inter-class are embedded in semi-supervised LPP algorithm to store the local structure of data, so as to maximize the similarities of the same class objects and differences of the different class objects. In this paper, the validity of the proposed feature extraction method is verified by 1-NN and SVM classifiers. With two public hyperspectral image datasets of Indian Pines and Pavia University, the proposed feature extraction method is verified effectively. The experimental results of GLSSFE show that the highest overall classification reaches 89.10% and 92.09% respectively. It is superior to the existing feature extraction algorithm, effectively mining global features and local features of hyperspectral images, enhancing object classification effect.

Key words: hyperspectral images, semi-supervised global discriminant analysis, semi-supervised local sparse preserving, feature extraction, spatial correlation