Shen Xiaole, Yu Chen, Lin Lin, Cao Jinzhou
College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China.
PeerJ Comput Sci. 2023 Aug 30;9:e1566. doi: 10.7717/peerj-cs.1566. eCollection 2023.
Buildings, which play an important role in the daily lives of humans, are a significant indicator of urban development. Currently, automatic building extraction from high-resolution remote sensing images (RSI) has become an important means in urban studies, such as urban sprawl, urban planning, urban heat island effect, population estimation and damage evaluation. In this article, we propose a building extraction method that combines bottom-up RSI low-level feature extraction with top-down guidance from prior knowledge. In high-resolution RSI, buildings usually have high intensity, strong edges and clear textures. To generate primary features, we propose a feature space transform method that consider building. We propose an object oriented method for high-resolution RSI shadow extraction. Our method achieves user accuracy and producer accuracy above 95% for the extraction results of the experimental images. The overall accuracy is above 97%, and the quantity error is below 1%. Compared with the traditional method, our method has better performance on all the indicators, and the experiments prove the effectiveness of the method.
建筑物在人类日常生活中扮演着重要角色，是城市发展的重要指标。目前，从高分辨率遥感影像（RSI）中自动提取建筑物已成为城市研究中的重要手段，如城市扩张、城市规划、城市热岛效应、人口估算和损害评估等。在本文中，我们提出了一种将自下而上的RSI低层次特征提取与来自先验知识的自上而下引导相结合的建筑物提取方法。在高分辨率RSI中，建筑物通常具有高强度、强边缘和清晰纹理。为了生成主要特征，我们提出了一种考虑建筑物的特征空间变换方法。我们提出了一种面向对象的高分辨率RSI阴影提取方法。对于实验图像的提取结果，我们的方法实现了用户精度和生产者精度均高于95%。总体精度高于97%，数量误差低于1%。与传统方法相比，我们的方法在所有指标上都具有更好的性能，实验证明了该方法的有效性。
