OPTIMIZING OBJECT-BASED CLASSIFICATION IN URBAN ENVIRONMENTS USING VERY HIGH RESOLUTION GEOEYE-1 IMAGERY

Abstract. The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In fact, one of the most common applications of remote sensing images is the extraction of land cover information for digital image base maps by means of classification techniques. When VHR satellite images are used, an object-based classification strategy can potentially improve classification accuracy compared to pixel based classification. The aim of this work is to carry out an accuracy assessment test on the classification accuracy in urban environments using pansharpened and panchromatic GeoEye-1 orthoimages. In this work, the influence on object-based supervised classification accuracy is evaluated with regard to the sets of image object (IO) features used for classification of the land cover classes selected. For the classification phase the nearest neighbour classifier and the eCognition v. 8 software were used, using seven sets of IO features, including texture, geometry and the principal layer values features. The IOs were attained by eCognition using a multiresolution segmentation approach that is a bottom-up regionmerging technique starting with one-pixel. Four different sets or repetitions of training samples, always representing a 10% for each classes were extracted from IOs while the remaining objects were used for accuracy validation. A statistical test was carried out in order to strengthen the conclusions. An overall accuracy of 79.4% was attained with the panchromatic, red, blue, green and near infrared (NIR) bands from the panchromatic and pansharpened orthoimages, the brightness computed for the red, blue, green and infrared bands, the Maximum Difference, a mean of soil-adjusted vegetation index (SAVI), and, finally the normalized Digital Surface Model or Object Model (nDSM), computed from LiDAR data. For buildings classification, nDSM was the most important feature attaining producer and user accuracies of around 95%. On the other hand, for the class "vegetation", SAVI was the most significant feature, obtaining accuracies close to 90%.