Methodology Development on Building Recognition and Change Detection Using Aerial Imagery

Abstract

In this study, we have developed a novel methodology for building change detection in the dense urban areas. Our approach is based on building recognition using aerial images and Digital Surface Models (DSMs) that allows detection of large and small buildings respectively. Large buildings are detected by a global thresholding of the DSMs using a height threshold in order to prevent the problem that one building may be divided into several portions. Small gable-roof and flat-roof buildings are detected individually according to the three-dimensional shape of the roofs so that buildings can be separated from each other more easily in densely built-up areas. Afterwards, change detection is implemented based on the result of building recognition, and only the DSMs are used for detecting the change of buildings in order to avoid the influence of image color variation. Also, in order to detect partially-changed buildings accurately, the increased and decreased height differences of two epochs of DSMs are extracted individually, and image morphological processing is performed to remove noise and extract actual changed areas. To assess the effectiveness of the proposed methodology, the change detection result has been verified by comparing to a visual interpretation result. The experimental results indicate 78.1% completeness with correctness of 52.3% in a dense built-up area, which demonstrate that our methodology can stably detect changed buildings with variation in height such as newly constructed, demolished, extended and structural alterations, and suppress false detection effectively.