Urban Land-Cover Classification with Façade Feature From Oblique Images

Abstract

In the remote sensing community, land-cover classification is usually performed on the top-view images. However, besides the top-view features (including elevation), facade captured by the oblique images is useful but severely underutilized in the land-cover classification. The facade information of an object, is by nature more variable, thus can be extremely useful when the extracted features are used for land-cover classification. Hence, in this paper, we try to explore the use of facade from the oblique images to enhance the accuracy of land-cover classification. Firstly, we locate the facades by finding the elevation changes and the corresponding above-ground objects. Then, the facade images are cropped from oblique images and the color and Haar-like features are extracted as facade features. Finally, following the object-based land-cover classification, super-pixels are generated and used as the basic unit for the feature extraction and classification. Experiments are performed on five representative site using five-head oblique aerial images and their derived orthophoto and digital surface model (DSM). The results show that with the facade information, the classification performances have been steadily improved, especially for the buildings which has around 10% improvement.