A Multiview Spectral–Spatial Feature Extraction and Fusion Framework for Hyperspectral Image Classification

Abstract

Hyperspectral images (HSIs) can provide abundant and diverse features which are helpful for classification, such as spectral, texture, and shape features. Combining these features can enhance the ability to describe the characteristics of different classes of land covers. However, most of the existing methods usually stack features from multiple views to construct high-dimensional data and then learn information from it which may waste information inherent in different feature spaces. In this letter, we proposed a multiview spectral–spatial feature extraction (FE) and fusion framework for the analysis and classification of the HSIs. First, different and complementary spatial features extracted by extended multiattribute profiles (EMAPs), gray-level cooccurrence matrix (GLCM), and Gabor from the original HSI are, respectively, stacked with the spectral bands to construct multiview data set for one single scene of HSI. Thus, each sample can be represented in different spectral–spatial domains. Then, a semisupervised FE method, which combines local fisher discriminant analysis (LFDA) that explores discriminative information from limited labeled samples and the improved neighborhood preserving embedding (NPE) that aims at maintaining the local neighborhood structure from a global perspective, is applied on the multiview data set to eliminate redundant information and obtain multiview spectral–spatial features. Note that the improved NPE which adds spatial interpixel correlations to similarity measure between samples is applied on all the samples rather than unlabeled samples, and hence, the best spatial nearby neighbors for each sample could be found from the whole data. Finally, we integrate these multiview features with diversity and complementarity to construct the intact feature representation for each sample. The experimental results show that the proposed method outperforms the state-of-the-art multiview FE methods for HSI classification.