Land-cover classification with an expert classification algorithm using digital aerial photographs

Abstract

The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1) bare soil, (2) cereals, including maize (Zea mays L.), oats (Avena sativa L.), rye (Secale cereale L.), wheat (Triticum aestivum L.) and barley (Hordeun vulgare L.), (3) high protein crops, such as peas (Pisum sativum L.) and beans (Vicia faba L.), (4) alfalfa (Medicago sativa L.), (5) woodlands and scrublands, including holly oak (Quercus ilex L.) and common retama (Retama sphaerocarpa L.), (6) urban soil, (7) olive groves (Olea europaea L.) and (8) burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.