Automated Detection of Martian Dune Fields

An approach for the automated detection of dune fields on remotely sensed images of the surface of Mars is presented in this letter. It is based on the extraction of local information from images (i.e., gradient features), which, in turn, is tested with boosting and support vector machine classifier...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2011-07, Vol.8 (4), p.626-630
Main Authors: Bandeira, L., Marques, J. S., Saraiva, J., Pina, P.
Format: Article
Language:English
Subjects:
Automated
Boosting
Dunes
Feature extraction
histogram of oriented gradient (HOG) features
Histograms
Mars
Mars Global Surveyor
Mars orbiter camera
Pixel
Planetary probes
Remote sensing
support vector machine (SVM)
Support vector machines
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Summary:An approach for the automated detection of dune fields on remotely sensed images of the surface of Mars is presented in this letter. It is based on the extraction of local information from images (i.e., gradient features), which, in turn, is tested with boosting and support vector machine classifiers. A detection rate of about 95% is obtained for fivefold cross validation on a set of 78 panchromatic images captured by the Mars Orbiter Camera of the Mars Global Surveyor probe on different locations of the planet.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2010.2098390