A hierarchical superpixel aggregation model for hyperspectral image

Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step merging procedure is designed in the hierarchical wise from local to global comparisons. Aiding by a density peak metric, which is to exploit the spectral correlation in hyperspectral image, the similar neighbor superpixels are merged firstly, and then the similar regions in discontinuous spatial location are gathered. Experimental results show that the proposed model can achieve high accuracy in low region number compared with original superpixel algorithm, and the performance for unsupervised classification application is also remarkable.