dc.contributor.author | Tso, B. | |
dc.contributor.author | Olsen, R.C. | |
dc.date.accessioned | 2014-12-05T19:26:38Z | |
dc.date.available | 2014-12-05T19:26:38Z | |
dc.date.issued | 2005-05 | |
dc.identifier.citation | International Journal of Remote Sensing, Volume 26, No. 10, May 2005, pp. 2113-2133 | |
dc.identifier.uri | http://hdl.handle.net/10945/43860 | |
dc.description.abstract | Unsupervised classification methodology applied to remote sensing image
processing can provide benefits in automatically converting the raw image data
into useful information so long as higher classification accuracy is achieved. The
traditional k-means clustering scheme using spectral data alone does not perform
well in general as far as accuracy is concerned. This is partly due to the failure to
take the spatial inter-pixels dependencies (i.e. the context) into account, resulting
in a â busyâ visual appearance to the output imagery. To address this, the hidden
Markov models (HMM) are introduced in this study as a fundamental
framework to incorporate both the spectral and contextual information in
analysis. This helps generate more patch-like output imagery and produces
higher classification accuracy in an unsupervised scheme. The newly developed
unsupervised classification approach is based on observation-sequence and
observation-density adjustments, which have been proposed for incorporating
2D spatial information into the linear HMM. For the observation-sequence
adjustment methods, there are a total of five neighbourhood systems being
proposed. Two neighbourhood systems were incorporated into the observationdensity
methods for study. The classification accuracy is then evaluated by means
of confusion matrices made by randomly chosen test samples. The classification
obtained by k-means clustering and the HMM with commonly seen strip-like and
Hilbert-Peano sequence fitting methods were also measured. Experimental results
showed that the proposed approaches for combining both the spectral and spatial
information into HMM unsupervised classification mechanism present improvements
in both classification accuracy and visual qualities. | en_US |
dc.rights | This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted. | en_US |
dc.title | Combining spectral and spatial information into hidden Markov models for unsupervised image classifciation | en_US |
dc.type | Article | en_US |
dc.contributor.department | Physics | |
dc.description.funder | The authors would like to convey their gratitude to the Remote Sensing Laboratory, Naval Postgraduate School, USA, for providing facilities in supporting this research project. | en_US |