Computational Science Technical Note CSTN-071

CSTN Home

Clusters in Hyper-cubic Multi-Channel Satellite Imagery

K. A. Hawick

Archived January 2009, Revised September 2010

Abstract

Multi-spectral remotely-sensed data such as satellite imagery can yield excellent insights into complex phenomena such as weather systems. Analysing the multi-channel space to separate out different features still presents a challenge, which may increase with the availability of hyper-spectral satellites. We use component labelling and population thresholding techniques to separate out clusters in hyper-dimensional channel space and use this information to identify different cloud types in geostationary satellite imagery. Three dimensional visualisation techniques are used to study the hyper-dimensional channel population data.

Keywords: remote sensing; multi-spectral imaging; visualization; component labelling.

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@INPROCEEDINGS{CSTN-071,
  author = {K. A. Hawick},
  title = {Clusters in Hyper-cubic Multi-Channel Satellite Imagery},
  booktitle = {Proc. Int. Conf. Computer Graphics and Imaging},
  year = {2011},
  pages = {62-69},
  address = {Innsbruck, Austria},
  month = {14-16 February},
  publisher = {IASTED},
  doi = {10.2316/P.2011.722-020},
  institution = {Massey University},
  timestamp = {2009.02.28}
}


[ CSTN Index | CSTN BiBTeX ]