k-means on a log-Cholesky Manifold, with Unsupervised Classification of Radar Products

08/08/2020

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We state theoretical properties for k-means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space, that provides a natural and favourable representation of these data. We then provide a novel application for this method, to time-series clustering of pixels in a sequence of Synthetic Aperture Radar images, via their finite-lag autocovariance matrices.

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k-means on a log-Cholesky Manifold, with Unsupervised Classification of Radar Products

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k-means on a log-Cholesky Manifold, with Unsupervised Classification of Radar Products

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