Spatial Subspace Clustering for Hyperspectral Data Segmentation

The authors propose a novel method called Spatial Subspace Clustering (SpatSC) for 1D hyperspectral data segmentation problem, e.g. hyperspectral data taken from a drill hole. Addressing this problem has several practical uses such as improving interpretability of the data, and especially a better understanding of the mineralogy. Spatial subspace clustering is a combination of subspace learning and the fused lasso. As a result, it is able to produce spatially smooth clusters. From this point of view, it can be simply interpreted as a spatial information guided subspace learning algorithm.

Provided by: The Society of American Magicians Topic: Big Data Date Added: Jan 2013 Format: PDF

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