Spectral–spatial classification of hyperspectral images by algebraic multigrid based multiscale information fusion

ABSTRACT

In this work, we present a novel spectral-spatial classification framework of hyperspectral images (HSIs) by integrating the techniques of algebraic multigrid (AMG), hierarchical segmentation (HSEG) and Markov random field (MRF). The proposed framework manifests two main contributions. First, an effective HSI segmentation method is developed by combining the AMG-based marker selection approach and the conventional HSEG algorithm to construct a set of unsupervised segmentation maps in multiple scales. To improve the computational efficiency, the fast Fish Markov selector (FMS) algorithm is exploited for feature selection before image segmentation. Second, an improved MRF energy function is proposed for multiscale information fusion (MIF) by considering both spatial and inter-scale contextual information. Experiments were performed using two airborne HSIs to evaluate the performance of the proposed framework in comparison with several popular classification methods. The experimental results demonstrated that the proposed framework can provide superior performance in terms of both qualitative and quantitative analysis.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61271408). The authors would like to thank D. Landgrebe from Purdue University for providing the AVIRIS image of Indian Pines and Paolo Gamba from University of Pavia for providing the ROSIS data set. The authors would also like to thank the Associate Editor and the anonymous reviewer for their valuable comments and suggestions, which significantly improved the quality of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [61271408];