Building damage detection after natural disasters like earthquakes is crucial for initiating effective emergency response actions. Remotely sensed very high spatial resolution (VHR) imagery can provide vital information due to their ability to map the affected buildings with high geometric precision. Many approaches have been developed to detect damaged buildings due to earthquakes. However, little attention has been paid to exploiting rich features represented in VHR images using Deep Neural Networks (DNN). This paper presents a novel super-pixel based approach combining DNN and a modified segmentation method, to detect damaged buildings from VHR imagery. Firstly, a modified Fast Scanning and Adaptive Merging method is extended to create initial over-segmentation. Secondly, the segments are merged based on the Region Adjacent Graph (RAG), considered an improved semantic similarity criterion composed of Local Binary Patterns (LBP) texture, spectral, and shape features. Thirdly, a pre-trained DNN using Stacked Denoising Auto-Encoders called SDAE-DNN is presented, to exploit the rich semantic features for building damage detection. Deep-layer feature abstraction of SDAE-DNN could boost detection accuracy through learning more intrinsic and discriminative features, which outperformed other methods using state-of-the-art alternative classifiers. We demonstrate the feasibility and effectiveness of our method using a subset of WorldView-2 imagery, in the complex urban areas of Bhaktapur, Nepal, which was affected by the Nepal Earthquake of April 25, 2015.

翻译：在自然灾害(如地震)发生后,在地震等自然灾害发生后进行破坏探测,对于启动有效的应急行动至关重要。遥感的甚高空间分辨率(VHR)图像能够提供重要信息,因为它们能够以高几何精确度绘制受影响建筑物的地图。已经开发了许多方法,以探测地震造成的受损建筑物。然而,很少注意利用VHR图像中的丰富特征,利用深神经网络(DNN)来利用VHR图像(DNN)来利用VNN(DNN)和经修改的分解法来探测受损建筑物。本文介绍了一种新型的超级像素基础方法。首先,一种经过修改的快速扫描和适应合并方法可以用来制造初步的过度分隔。第二,这些部分在区域相邻图(RAG)的基础上进行合并,认为这是一种改进的语义相似性标准,由当地二进制图(LBBPP)、光谱和形状特征组成。第三,用SDAE-DNNN(S-DNN)-DS-DS-DS-DS-D)的精度结构特性特性特性特性特征,通过我们内部的精细化的精度图像的精度分析方法来学习。