Journal of Geo-information Science >
Research on Extracting Building Height Rapidly Based on High-resolution Remote Sensing Images Without Parameters
Received date: 2014-10-22
Request revised date: 2014-11-18
Online published: 2015-08-05
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There is a pressing need to extract building height rapidly based on high-resolution remote sensing images without parameters in the field of urban construction and land management. Current studies are mostly based on remote sensing images with parameters, however the images used for extraction are difficult to get, and current extraction methods have a lot of restrictions. In this paper, a new method is proposed with the use of four types of characteristic lines. The characteristic lines are comprehensively formed by the characteristic points on a single image, which is used to convert the building height based on high-resolution images without parameters. The four types of characteristic lines include: the connection line of the roof displacement point and roof shadow point, the displacement of the roof image point caused by the building height, the full-length shadow, the remaining length of the shadow excluding the part occluded by building. Four types of calculation model for acquiring building height based on the corresponding characteristic lines are deduced. According to the known heights of a small amount of constructions and using the four calculation models, the relevant parameters of remote sensing images can be derived conversely. Then, we can select the characteristic lines that are extracted most accurately on each building , and use the corresponding model to convert the building height. With the application of this method, a large number of building heights can be calculated quickly and accurately. The method is verified based on Google Earth image in Nanjing city and the results show: the images used in this approach are easy to acquire; the method of comprehensive measurement and calculation does not merely rely on the use of shadow lengths to calculate building height, so it significantly increases the practicality of extracting building height on a single image; it solves the issue that there is no related angle parameters of the sun and the satellite position when calculating the building height. Case study indicated that the precision of the proposed method is high, and it can extract the building height quickly in a large area. Generally, the method proposed in this paper has significant practical value in production applications.
QIAO Weifeng , LIU Yansui , XIANG Lingzhi , WANG Yahua . Research on Extracting Building Height Rapidly Based on High-resolution Remote Sensing Images Without Parameters[J]. Journal of Geo-information Science, 2015 , 17(8) : 995 -1000 . DOI: 10.3724/SP.J.1047.2015.00995
Fig. 1 Schematic diagram of satellite imaging图1 卫星成像示意图 |
Fig. 2 Comparison of graphs before and after applying the image enhancement图2 影像增强处理前后对比图 |
Tab. 1 Difficulty level analysis of characteristic line extractions表1 特征线段提取难易度分析 |
特征线段种类 | 提取影响因素 | 提取难易度 |
---|---|---|
l1 | 建筑密集区的建筑阴影易被周围建筑遮挡;当待测建筑物周围有水面和植被时,建筑物阴影和这些低灰度值区不易区分;当太阳和卫星处于建筑同侧时,阴影全长易被建筑本身的投影遮挡 | 阴影全长提取中问题较多,使用较少 |
l2 | 卫星高度角大于80°时屋顶像点位移不明显;当高层建筑下面有裙楼时,屋顶对应的地面点不易确定 | 提取较方便,使用较多 |
l3 | 阴影中的任意角点和与之匹配的屋顶角点成像点选择较方便 | 提取较方便,使用较多 |
l4 | 影响因素和l1提取相近 | 提取中问题较多,计算中涉及到δ角度,需另外量算,一般不使用 |
The authors have declared that no competing interests exist.
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