Identifying damaged buildings from high-resolution satellite imagery in hazardous areas using morphological operators

This paper presents a methodology and results of evaluating damaged buildings detection algorithms using an object recognition task based on Differential Morphological Profile (DMP) for Very High Resolution (VHR) remotely sensed images. The proposed approach involves several advanced morphological operators among which an adaptive hit-or miss transform with varying size, shape and gray level of the structuring elements. IKONOS satellite panchromatic images consisting of a pre and post-earthquake site of the Sichuan area in China were used. Morphological operations of opening and closing with constructions are applied for segmented images. The Unsupervised classification I SOD AT A algorithm is used for the feature extraction and the results comparison with ground truth data, complex urban area before the earthquake gives 76% and same area wracked after the earthquake gives 88% buildings detection on object based accuracy.