Object-based image analysis for urban land cover classification in the city of Campinas - SP, Brazil

Classifiers that make use of pixel-by-pixel approaches are limited in the high spatial and radiometric resolution of urban areas, that happens mostly because of the similarity between the target´s spectral response like ceramic roofs and bare soil. Because of that, the literature favors approaches that make use of object-oriented analysis for image interpretation, those approaches make a better use of the high spatial resolution and do not use only the target spectral response. Assuming that the object-oriented analysis is a favorable approach to be employed for intra-urban image classification, this paper will assess the results of such approach through an implementation of it in an urbanized area from the city of Campinas (Brazil), which has a size close to twelve square kilometers. Making use of the fusion of high spatial resolution image from Worldview-2 sensor and it´s panchromatic band, the experiments were performed with the use of eCognition Developer 8 as the segmentation platform, and the classification being based on a decision tree generated by J48 (C4.5) algorithm on the software WEKA. This work also assess which approach best suits the experiment needs, being an optimal attribute selection achieved through a Wrapper filter, with a final kappa statistic of 0.9425.