Modified Structure-Aware Collaborative Representation for Hyperspectral Image Classification

Collaborative representation (CR) is an efficient method for hyperspectral image classification. There exists structure-aware CR with Tikhonov regularization (SaCRT) that utilizes the class label information of training samples into estimation of representation coefficients to provide better performance. It can be further enhanced by considering spatial features because neighboring pixels around the central pixel tend to belong to the same class with high probability. In this paper, a modified SaCRT is proposed for hyperspectral image classification. Its performance is analyzed on different types of spatial features (i.e., spatial averaging features), global feature (i.e., Gabor feature), shape features (i.e., derivative of extended morphological profile (DMP) features), and edge preserving feature. In addition, a majority voting-based ensemble technique is used to enhance the performance by combining different features. The experimental results illustrate that the proposed approach can yield better performance in comparison to state-of-the-art classifiers.