Publications des scientifiques de l'IRD

Bayoudh M., Roux Emmanuel, Richard G., Nock R. (2015). Structural knowledge learning from maps for supervised land cover/use classification : application to the monitoring of land cover/use maps in French Guiana. Computers and Geosciences, 76, p. 31-40. ISSN 0098-3004.

Titre du document
Structural knowledge learning from maps for supervised land cover/use classification : application to the monitoring of land cover/use maps in French Guiana
Année de publication
2015
Type de document
Article référencé dans le Web of Science WOS:000349735900004
Auteurs
Bayoudh M., Roux Emmanuel, Richard G., Nock R.
Source
Computers and Geosciences, 2015, 76, p. 31-40 ISSN 0098-3004
The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.
Plan de classement
Télédétection [126]
Description Géographique
GUYANE FRANCAISE
Localisation
Fonds IRD [F B010063972]
Identifiant IRD
fdi:010063972
Contact