Building detection in disaster area is pivotal in collecting disaster information and implementing post disaster rescue. Aiming at detecting buildings in disaster area from remote sensing images wtih high-resolution, an improved multi-directional and multi-scale segmentation algorithm based on morphological features is proposed to implement automated detection of buildings in disaster area. Firstly, we integrate the properties of morphological operators (e.g., reconstruction, granularity, and directionality) into the implicit characteristics of buildings (e.g. , brightness, size, and contrast) to extract bright and high-contrast buildings. Then, the regional image edge information is combined to extract potential buildings. Experimental results show that the proposed method has a higher detection rate and a low false rate in detecting buildings of disaster area.