Research Papers

Spatial subspace clustering for drill hole spectral data

[+] Author Affiliations
Yi Guo

CSIRO Computational Informatics, North Ryde, NSW 1670, Australia

Junbin Gao

Charles Sturt University, School of Computing and Mathematics, Bathurst, NSW 2795, Australia

Feng Li

Academy of Opto-Electronics, Earth Observation Technology Application Department, CAS, Beijing 100094, China

J. Appl. Remote Sens. 8(1), 083644 (Apr 28, 2014). doi:10.1117/1.JRS.8.083644
History: Received January 28, 2014; Revised March 9, 2014; Accepted March 11, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Yi Guo ; Junbin Gao and Feng Li
"Spatial subspace clustering for drill hole spectral data", J. Appl. Remote Sens. 8(1), 083644 (Apr 28, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083644


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