Patch-Based Diffusion Learning for Hyperspectral Image Clustering

An algorithm for clustering hyperspectral images (HSI) based on diffusion geometry in the space of high-dimensional image patches is proposed. By using the patch structure of the HSI, robustness to noise is achieved in the clustering process. Results on real hyperspectral data indicate the effectiveness of working in the space of HSI patches, compared to working in the space of HSI pixels.