Morphological building indexes (MBI) have proven to be effective tools for automated building spatial-feature-extraction tasks in images from urban areas. However, owing to the intrinsic shortcomings of MBI, commission and omission errors occur in regions with spectral properties similar to those of buildings and dark heterogeneous roofs, respectively. Some targets (such as bright bare land or roads) can cause substantial interference, which poses an even greater challenge in performing accurate building detection from images of complex environments. In this study, a new automated building detection approach based on a morphological attribute profile is presented with the goal of reducing commission and omission errors. As the first step, corners are detected in very high-resolution (VHR) remote sensing images through an automatic optimization procedure, and weighted spatial voting is performed to predict the presence of built-up areas. Then, by investigating the properties between the attribute filters and buildings, a novel morphological attribute building index is constructed by considering the extracted built-up area as an input image. To validate the detection performance, the approach was tested using VHR images with 1-m spatial resolution. The quantitative assessment indicates that the proposed approach improves the building detection accuracy in images of complex environments.

事实证明，形态学建筑指数（MBI）是有效的工具，可用于自动构建市区图像中的空间特征提取任务。但是，由于MBI的固有缺点，在光谱特性分别类似于建筑物和深色异质屋顶的区域中会发生委托和遗漏错误。某些目标（例如明亮的空地或道路）可能会造成严重的干扰，这在根据复杂环境的图像进行准确的建筑物检测中提出了更大的挑战。在这项研究中，提出了一种新的基于形态属性轮廓的自动建筑物检测方法，旨在减少委托和遗漏错误。第一步 通过自动优化程序在超高分辨率（VHR）遥感图像中检测到角落，并执行加权空间投票以预测建筑物区域的存在。然后，通过研究属性过滤器和建筑物之间的属性，通过将提取的建筑物区域视为输入图像来构造新的形态属性建筑物索引。为了验证检测性能，使用具有1 m空间分辨率的VHR图像对该方法进行了测试。定量评估表明，所提出的方法提高了复杂环境图像中建筑物检测的准确性。通过将提取的建筑物区域视为输入图像，构造了新颖的形态属性建筑物指数。为了验证检测性能，使用具有1 m空间分辨率的VHR图像对该方法进行了测试。定量评估表明，所提出的方法提高了复杂环境图像中建筑物检测的准确性。通过将提取的建筑物区域视为输入图像，构造了新颖的形态属性建筑物指数。为了验证检测性能，使用具有1 m空间分辨率的VHR图像对该方法进行了测试。定量评估表明，所提出的方法提高了复杂环境图像中建筑物检测的准确性。