Extended Subspace Projection Upon Sample Augmentation Based on Global Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

Abstract

Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. To address the earlier issues, the classification models such as subspace-based support vector machines, which have gained a certain advance but mainly concentrate on the dimensionality reduction and ignore the augmentation of training samples. In fact, these two issues are equally important for improving the performance of classification, and should be addressed simultaneously. Therefore, this article proposes a novel method named extended subspace projection upon sample augmentation based on global spatial and local spectral similarity (GLSC) for HSIC, which takes both sample augmentation and dimensionality reduction into consideration. Specifically, it first exploits the GLSC to enlarge the original labeled sample set, which allows HSIC to obtain more prior information. Then, the augmented samples and the original labeled samples are combined to construct the extended subspace, which is more comprehensive to reflect the real situation of the ground objects. Finally, the original HSI is projected to the subspace and classified by the neighborhood activity degree-driven representation-based classifier. Experimental results on three real hyperspectral datasets demonstrate the practicality and effectiveness of the proposed method for HSIC tasks.

Keywords

• Dimensionality reduction
• hyperspectral remote sensing
• labeled sample size
• supervised classification