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Abstract
This paper presents a novel study of the classification of
large-scale Mars McMurdo panorama image. Three dimensionality
reduction techniques, based on fuzzy-rough sets,
information gain ranking, and principal component analysis
respectively, are each applied to this complicated image
data set to support learning effective classifiers. The
work allows the induction of low-dimensional feature subsets
from feature patterns of a much higher dimensionality.
To facilitate comparative investigations, two types of image
classifier are employed here, namely multi-layer perceptrons
and K-nearest neighbors. Experimental results
demonstrate that feature selection helps to increase the
classification efficiency by requiring considerably less features,
while improving the classification accuracy by minimizing
redundant and noisy features. This is of particular
significance for on-board image classification in future
Mars rover missions.
Original language | English |
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Pages | 1419-1424 |
Number of pages | 6 |
Publication status | Published - 2009 |
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Dive into the research topics of 'Effective Feature Selection for Mars McMurdo Terrain Image Classification'. Together they form a unique fingerprint.Projects
- 1 Finished
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Stereo Wide Angle Cameras for the EXOMARS Panoramic Camera Instrument - Part A (see 10448)
Barnes, D. (PI)
Science and Technology Facilities Council
01 Oct 2010 → 31 Mar 2013
Project: Externally funded research