面向高光谱图像分类的空谱半监督局部判别分析

Spatial-Spectral Semi-Supervised Local Discriminant Analysis for Hyperspectral Image Classification

针对传统的基于特征提取的高光谱图像分类算法大多只考虑光谱信息而忽略空间信息的问题,提出了一种基于空谱半监督局部判别分析(S3 ELD)和空谱最近邻(SSNN)分类器的高光谱图像分类算法。该算法结合高光谱图像的空间一致性,在利用标记样本的判别信息保持数据集可分性的基础上,定义空间近邻像元散度矩阵来保存像元的空间近邻结构,提出基于空谱距离的相似性度量并将其应用于局部流形结构的发现和SSNN的构建。S3 ELD算法不仅能揭示数据集的局部几何关系,而且增强了光谱域同类像元和空间域近邻像元在低维嵌入空间的聚集性。结合SSNN进行分类,进一步提升了分类精度。利用PaviaU和Salinas数据集进行的实验结果表明,S3 ELD算法的总体分类精度分别达到了92.51%和96.29%;与现有几种算法相比,该算法能更有效地提取出判别特征信息,并达到更高的分类精度。

In traditional hyperspectral image classification algorithm based on feature extraction,spectral information is usually considered while spatial information is ignored.To address this problem,a hyperspectral image classification algorithm based on semi-supervised spatial-spectral local discriminant analysis（S3ELD）and spatial-spectral nearest neighbor（SSNN）classifier is proposed in this paper.Combining the spatial consistency of hyperspectral images and on the basis that the discriminant information of the labeled samples is used to maintain the separability of the data set,we define the spatial local pixel scatter matrix to preserve the spatial-domain neighborhood structures of pixel.A similarity measure method based on the spatial-spectral distance is then proposed to discover the local manifold structure and to construct SSNN.S3 ELD algorithm not only reveals the local geometric relations of the data set but also enforces the compactness of the spectral-domain same class pixels and the spatial-domain local neighbor pixels in the low-dimension embedding space.Combining SSNN to classify,the classification accuracy is further enhanced.The experiments on the PaviaU and Salinas data sets show that the overall classification accuracy of S3 ELD algorithm reaches 92.51% and96.29%,respectively.Compared with several existing algorithms,the proposed algorithm can efficiently extract the information of discriminant characteristics and obtain higher classification accuracy.