Multilayer graph spectral analysis for hyperspectral images

Abstract Hyperspectral imaging has broad applications and impacts in areas including environmental science, weather, geo/space exploration. The intrinsic spectral–spatial structures potential multi-level features different frequency bands make multilayer graph an intuitive model for hyperspectral images (HSI). To study the underlying characteristics of HSI to take advantage signal processing (GSP) tools, this work proposes a spectral analysis based on (M-GSP). More specifically, we present (MLG) models tensor representations HSI. By exploring space, develop MLG-based methods applications, unsupervised segmentation supervised classification. Our experimental results demonstrate strength M-GSP information extraction.