Modern hyperspectral imaging sensor technology provides detailed spectral and spatial information that enables precise analysis of land cover usage. From a research point of view, traditional widely used statistical models are often limited in the sense that they do not incorporate some of the useful angle information contained in the feature vectors, and hence alternative modeling methods are required. In the study to be presented, the use of cosine angle information and its embedding onto a spherical manifold is investigated. The transformation of hyperspectral images onto a unit hyperspherical manifold is achieved by using the recently proposed spherical local embeddings approach. Spherical local embeddings is a method that computes high-dimensional local neighborhood preserving coordinates of data on constant curvature manifolds. We further develop a novel Kent mixture model for unsupervised classification of embedded cosine pixel coordinates. A Kent distribution is one of the natural models for multivariate data on a spherical manifold. Parameters for the model are estimated using the Expectation-Maximization procedure. The mixture model is applied to two different Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) datasets that were acquired from the Tippecanoe County in Indiana. The results obtained present insights on cosine pixel coordinates and also serve as a motivation for further development of new models to analyze hyperspectral images in spherical manifolds.

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