ZONAL MAPPING OF URBAN AREAS USING MEIS-I I AIRBORNE DIGITAL IMAGES

The aim of this study is to evaluate the contribution of texture infor mation to high spatial and spectral resolution for zonal mapping in ur ban areas. The airborne data used were acquired over the Montreal Urba n Community (Montreal, Canada) by the Multi-detector Electro-optical I maging Scanner (MEIS-II) of the Canada Centre for Remote Sensing. Spec tral analysis of the divergence between classes has shown that bands 3 (847-901 nm), 4 (622-659 nm); and 7 (433-463 mm) offer the optimal co mbination for discriminating between the urban classes we have defined . Textural features derived from three different order histograms were calculated from band 4 and evaluated in terms of their ability to dis criminate between urban classes. We then extracted the best feature fr om each of the histograms. The integration of these three textural fea tures with the three spectral bands has shown that textural informatio n permits an improvement in class separability resulting in an increas e in the rate of correct classification in the order of 12%. This incr ease is however dependent on the type of class and varies from 4.5% fo r forest and parks to 16.8% for urban areas with low vegetation densit y. Classification by maximum likelihood of these spectral-textural dat a and the visual analysis of results also show that textural informati on, in conjunction with high spatial and spectral resolution, provides appropriate zonal mapping of urban areas.