Automated detection of refugee dwellings from satellite imagery using multi-class graph-cut segmentation and shadow information

This paper addresses the automated detection and enumeration of the dwellings/tents within the refugee camp areas using satellite and aerial images. The fact that dwellings inside the refugee camps correspond to very small, densely and sometimes arbitrarily positioned objects that are hard to distinguish from the background, even in Very-High Resolution (VHR) images, makes the automated tent/dwelling detection a difficult problem. In this paper, a method combining the use of the multi-class graph-cut segmentation and shadow information is proposed for dwelling/tent detection. Accordingly, as a first step, tents/dwellings are detected by applying graph-cut segmentation and morphology. Then, in order to improve the accuracy, the shadow information is exploited. At the last step, all detected tent segments are re-checked for correctness using a gradient-based thresholding method. The proposed method is tested on the images of the refugee camps that mostly have arbitrary and dense dwelling/tent positioning inside. The precision and recall values are determined to evaluate the performance. The obtained average precision (91.9%) and average recall (90.0%) performances are promising considering the difficulty of the problem.