Morphological Building/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas
Morphological Building/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas
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DOI：
10.1109/jstars.2011.2168195
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发表时间：
2012-02-01
影响因子：
5.5
通讯作者：
Zhang, Liangpei
中科院分区：
文献类型：
--
作者：
Huang, Xin;Zhang, Liangpei
形态学建筑指数（MBI）是一种近期发展起来的用于在高分辨率影像中自动识别建筑物的方法。然而，由于建筑物、裸土和道路之间具有相似特征，MBI容易出现错分误差。此外，在深色且不均匀的屋顶处会出现漏分误差。在本研究中，提出了一个从高分辨率影像中提取建筑物的系统框架，旨在减少原始MBI算法的错分和漏分误差。改进包括三个方面：1）提出一种形态学阴影指数（MSI）来检测阴影，将其用作建筑物的空间约束；2）提出一种双阈值滤波方法来整合MBI和MSI的信息；3）所提出的框架在基于对象的环境中实施，其中使用几何指数和植被指数来去除狭窄道路和明亮植被产生的噪声。所提出的框架在华盛顿特区国家广场的1米分辨率的伊克诺斯影像以及中国东部杭州的2米分辨率的8波段 worldview - 2影像上进行了验证。通过与地面真值参考进行比较，结果表明我们的方法在两个数据集上对建筑物和背景的区分都达到了90%以上的总体精度。在对比研究中，发现所提出的方法显著改进了原始MBI。此外，所提出的方法比使用差分形态学剖面（DMP）和多尺度城市复杂度指数（MUCI）的支持向量机解译更准确。
Morphological building index (MBI) is a recently developed approach for automatic indication of buildings in high-resolution imagery. However, MBI is subject to commission errors due to the similar characteristics between buildings, bare soil and roads. Furthermore, omission errors occur in dark and heterogeneous roofs. In this study, a systematic framework for building extraction from high-resolution imagery is proposed, aiming to alleviate both commission and omission errors for the original MBI algorithm. The improvements include three aspects: 1) a morphological shadow index (MSI) is proposed to detect shadows that are used as a spatial constraint of buildings; 2) a dual-threshold filtering is proposed to integrate the information of MBI and MSI; 3) the proposed framework is implemented in an object-based environment, where a geometrical index and a vegetation index are then used to remove noise from narrow roads and bright vegetation. The proposed framework was validated on an Ikonos image of Washington DC Mall with 1-m resolution and an 8-channel WorldView-2 image of Hangzhou, east of China, with 2-m resolution. By comparison with the ground truth references, it was shown that our method achieved over 90% overall accuracy for discrimination between buildings and backgrounds for both datasets. In the comparative study, it was revealed that the proposed method improved the original MBI significantly. Furthermore, the proposed method was more accurate than the support vector machine interpretation with the differential morphological profiles (DMP) and multiscale urban complexity index (MUCI).