  The problem of unsupervised learning and segmentation of hyperspectral images
is a significant challenge in remote sensing. The high dimensionality of
hyperspectral data, presence of substantial noise, and overlap of classes all
contribute to the difficulty of automatically clustering and segmenting
hyperspectral images. We propose an unsupervised learning technique called
spectral-spatial diffusion learning (DLSS) that combines a geometric estimation
of class modes with a diffusion-inspired labeling that incorporates both
spectral and spatial information. The mode estimation incorporates the geometry
of the hyperspectral data by using diffusion distance to promote learning a
unique mode from each class. These class modes are then used to label all
points by a joint spectral-spatial nonlinear diffusion process. A related
variation of DLSS is also discussed, which enables active learning by
requesting labels for a very small number of well-chosen pixels, dramatically
boosting overall clustering results. Extensive experimental analysis
demonstrates the efficacy of the proposed methods against benchmark and
state-of-the-art hyperspectral analysis techniques on a variety of real
datasets, their robustness to choices of parameters, and their low
computational complexity.
