(Article of periodic en Anglais - 2013)

Document title

Advances in automated detection of sand dunes on Mars

Authors(s) and Affiliation(s)

BANDEIRA L. (1) ; MARQUES J.S. (2) ; SARAIVA J. (1) ; PINA P. (1) ;
(1) CERENA-Centro de Recursos Naturais e Ambiente, Inst. Sup. Técnico, Lisboa, PORTUGAL
(2) ISR - Inst. de Sistemas e Robótica, Inst. Sup. Técnico, Lisboa, PORTUGAL


This paper describes advances in an automatic approach for the detection of sand dunes of Mars, based on supervised learning techniques. A set of features (gradient histogram) is extracted from the remotely sensed images and 2 classifiers (Support Vector Machine and Random Forests) are trained from this data. The evaluation is conducted on 230 MOC-NA images (spatial resolution between 1.45 and 6.80 m/pixel) leading to about 89% of correct detections. A detailed analysis of the detection results (dune/non-dune) is performed by dune type or bulk shape, confirming high performances independently of the way the dataset is analysed. This demonstrates the robustness and adequacy of the automated approach to deal with the large variety of aeolian structures present on the surface of Mars


Article of periodic

published at : Earth surface processes and landforms / ISSN 0197-9337 / CODEN ESPLDB

Editor : Wiley, Chichester - ROYAUME-UNI (1981)

Millesime : 2013, vol. 38, no3 [pp. 275-283]

Bibliographic references : 16 ref.

Collation : 12 fig., 4 tabl.



INIST-CNRS, Cote INIST : 17355

Digital Object Identifier

Go to electronic document thanks to its DOI : doi:10.1002/esp.3323

Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2013
Refdoc record number (ud4) : 27865136 : Permanent link - XML version
Powered by Pxxo