Hyperspectral image classification using multilayer superpixel graph and loopy belief propagation

In this paper, we propose a new method for hyperspectral image (HSI) classification using multi-layer superpixel graph and loopy belief propagation. A merging algorithm using graph based representation of image is applied to generate multi-scale superpixels in hyperspectral image at first. Then, we build a multi-layer superpixel graph and use loopy belief propagation to transmit messages between the superpixels and compute beliefs at each superpixel in our multi-layer graph for HSI classification. Experimental results with real hyperspectral data set demonstrate that our proposed method provides good performance and is competitive with some of the best available spectral-spatial methods for hyperspectral image classification.