3D Buildings Change Detection from Aerial and Satellite Stereo Imagery Using Kullback–Leibler Divergence Algorithm

Monitoring city sprawls is considered an essential subject in urban planning. The most important object in the urban areas is the building; therefore, finding an automatic method for detecting the changes in the buildings is considered a priority task for researchers to consider the changes in a district, especially for assessing the damages during disasters and updating geo-database. However, using 2D images to detect changes is ineffective because of the various imaging environments and the parameters of the sensors. Furthermore, during the change detection process, it is difficult to distinguish between the building and other objects due to the similarity of spectral properties. Therefore, it is necessary to use stereo images for DSM generation and then find the changes. This paper proposes a Kullback–Leibler divergence (KLD) algorithm to detect urban area changes based on stereo imagery. Two DSMs have been obtained through the photogrammetric process using two different sensors. The first data set is based on the stereo aerial imagery captured in 2012 and the second stereo is from the worldview-2 sensor captured in 2017. Before applying the KLD algorithm, the aerial’s DSM, which has an original resolution of 0.3m, was resampled to 1 m to make it similar to the satellite’s DSM. Three study areas have been selected for the algorithms test, located in Erbil-Iraq. The assessment shows that the KLD detected changes better than other methods after removing the small fragments through the post-processing step. For the evaluation, the confusion matrix has been determined for each study area. The analysis demonstrates that the overall accuracy for the three study areas where 89.3%, 91.1% and 88.9 %, respectively.