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Journal of Applied Remote Sensing

Spatial subspace clustering for drill hole spectral data
Author(s): Yi Guo; Junbin Gao; Feng Li
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Paper Abstract

A method called spatial subspace clustering (SpatSC) is proposed for the hyperspectral data segmentation problem focusing on the hyperspectral data taken from a drill hole, which can be seen as one-dimensional image data compared with hyperspectral/multispectral image data. Addressing this problem has several practical uses, such as improving interpretability of the data, and, especially, obtaining a better understanding of the mineralogy. SpatSC is a combination of subspace learning and the fused least absolute shrinkage and selection operator. 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. SpatSC has flexible structures that embrace the cases with and without library of pure spectra. It can be further extended, for example, using different error structures, such as including rank operator. We test this method on both simulated data and real-world hyperspectral data. SpatSC produces stable and continuous segments, which are more interpretable than those obtained from other state-of-the-art subspace learning algorithms.

Paper Details

Date Published: 28 April 2014
PDF: 20 pages
J. Appl. Rem. Sens. 8(1) 083644 doi: 10.1117/1.JRS.8.083644
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Yi Guo, Commonwealth Scientific and Industrial Research Organisation (Australia)
Junbin Gao, Charles Sturt Univ. (Australia)
Feng Li, Academy of Opto-Electronics (China)

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