Abstract The rapid and accurate detection of damaged buildings after an earthquake are critical for emergency response. Given the difference in the textures of damaged parts and those of the original buildings, damaged buildings can be accurately detected through textural heterogeneity. However, quantitatively detecting damaged buildings using such heterogeneity from post-earthquake images is difficult. Therefore, we propose a method of automatically extracting house damage information from post-quake high-resolution optical remote sensing imagery through the multiscale fusion of spectral and textural features, which can be achieved in three steps. Firstly, the textural and spectral features of the images are enhanced at the pixel level. Secondly, the resulting feature images are fused at the feature level and the fused feature images are segmented using superpixels. Lastly, a post-quake house damage index model is constructed. Results show an overall accuracy of 76.75%, 75.35% and 83.25% for three different types of imagery. This finding indicates that the proposed algorithm can be used to extract damage information from multisource remote sensing data and provide useful guidance for post-disaster rescue and assessment based on regional house damage conditions.

中文翻译：

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基于多尺度自适应多特征融合的地震破坏建筑物遥感检测新方法

摘要 地震后对受损建筑物的快速准确检测是应急响应的关键。由于受损部分的纹理与原始建筑物的纹理不同，因此可以通过纹理异质性准确检测受损建筑物。然而，使用地震后图像中的这种异质性定量检测受损建筑物是很困难的。因此，我们提出了一种通过光谱和纹理特征的多尺度融合从震后高分辨率光学遥感图像中自动提取房屋损坏信息的方法，该方法可以分三步实现。首先，在像素级增强图像的纹理和光谱特征。第二，生成的特征图像在特征级别融合，融合的特征图像使用超像素进行分割。最后，构建了震后房屋损坏指数模型。结果显示三种不同类型图像的总体准确度分别为 76.75%、75.35% 和 83.25%。这一发现表明，所提出的算法可用于从多源遥感数据中提取损坏信息，并为基于区域房屋损坏情况的灾后救援和评估提供有用的指导。