Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks

TL;DR

This paper introduces a sparse modeling framework for hyperspectral image classification that exploits spectral and spatial redundancies, reducing computational complexity and improving accuracy on benchmark datasets.

Contribution

It proposes a novel spectral block-based sparse modeling approach that enhances classification accuracy and efficiency compared to existing methods.

Findings

Improved classification accuracy on Pavia University and Indian Pines datasets.

Reduced computational time relative to state-of-the-art methods.

Effective exploitation of spectral and spatial redundancies.

Abstract

Hyperspectral images provide abundant spatial and spectral information that is very valuable for material detection in diverse areas of practical science. The high-dimensions of data lead to many processing challenges that can be addressed via existent spatial and spectral redundancies. In this paper, a sparse modeling framework is proposed for hyperspectral image classification. Spectral blocks are introduced to be used along with spatial groups to jointly exploit spectral and spatial redundancies. To reduce the computational complexity of sparse modeling, spectral blocks are used to break the high-dimensional optimization problems into small-size sub-problems that are faster to solve. Furthermore, the proposed sparse structure enables to extract the most discriminative spectral blocks and further reduce the computational burden. Experiments on three benchmark datasets, i.e., Pavia University Image and Indian Pines Image verify that the proposed method leads to a robust sparse modeling of hyperspectral images and improves the classification accuracy compared to several state-of-the-art methods. Moreover, the experiments demonstrate that the proposed method requires less processing time.