Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks
This work addresses computational challenges in hyperspectral image classification for material detection, offering an incremental improvement over existing methods.
The paper tackles hyperspectral image classification by proposing a sparse modeling framework that uses spectral blocks and spatial groups to exploit redundancies, reducing computational complexity and improving accuracy on benchmark datasets like Pavia University and Indian Pines, with reported gains in processing time.
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.