Hyperspectral image classification using a spectral–spatial random walker method

ABSTRACT

This article proposes a spectral–spatial method for classification of hyperspectral images (HSIs) by modifying traditional random walker (RW). The proposed method consists of suggesting two main modifications. First, to construct a spatial edge weighting function, low-frequency edge weighting function is proposed. In this function, the detail weights are removed. Second, to enhance the classification accuracy, a fusion of spectral and spatial Laplacian matrix in RW is suggested. This fusion can improve the classification performances compared to traditional RW using only spatial Laplacian matrix. In comparison with some of the state-of-the-art RW and spectral–spatial classifier methods, the experimental results of the proposed method (spectral–spatial RW) show that the proposed method significantly increases the classification accuracy of HSI.

Acknowledgements

The author would like to thank Prof. P. Gamba from the University of Pavia, Pavia, Italy, for providing the Pavia dataset and the referee committee members of the International Journal Of Remote Sensing for their constructive, pertinent, and invaluable comments/suggestions.

Disclosure statement

No potential conflict of interest was reported by the author.