A Framework for Improving Urban Land Cover Using Object and Pixel-Based Techniques via Remotely Sensed Data

Abstract

Recently, the advancement of remote sensing technology played a key role in urban land/cover mapping, planning, tourism, and environmental management. Images with a high spatial resolution for urban classification are widely used. Despite the high spectral resolution of the image, spectral confusion happens among different land covers. Furthermore, the shadow problem also causes poor results in the classification based on traditional per-pixel spectral approaches. This study looks at ways of improving the classification of urban land cover using QuickBird images. Maximum likelihood (ML) pixel-based supervised as well as Rule-based object-based approaches were examined on high-resolution QuickBird satellite images in Karbala City, Iraq. This study indicates that the use of textural attributes during the rule-based classification procedure can significantly improve land-use classification performance. Furthermore, the results show that rule-based results are highly effective in improving classification accuracy than pixel-based. The results of this study provide further clarity and insight into the implementation of using the object-based approach with various classifiers for the extended study. In addition, the finding demonstrated the integration of high-resolution QuickBird data and a set of attributes derived from the visible bands and geometric rule set resulted in superior class separability, thus higher classification accuracies in mapping complex urban environments.