Classification d'images radar aéroporté multipolarisations en milieu agricole

Abstract

We have examined the contribution of multipolarized airborne radar data for the discrimination of crops. An unsupervised classification algorithm and a maximum likelihood supervised classification were used and compared. The results show that multipolarized radar data offer an accurate means of identifying crops. The average classification accuracies were 83 and 79 per cent for the supervised and unsupervised methods respectively. Comparison of the two methods using the same data suggests that the unsupervised method gives essentially similar results to those using the supervised classification method; however, the unsupervised method requires far less field effort and computer time.