基于双张影像的城市建筑物高度提取方法

针对现有城市建筑物高度提取方法存在的问题,提出一种基于双张影像的城市建筑物高度简易提取方法。利用高分辨率遥感影像和建筑物立面影像,分别提取样本建筑物的阴影长度和高度,采用最小二乘法解算出影像阴影和建筑物高度的相关公式,通过阴影长度反算出建筑物的高度,从而实现大批量建筑物高度数据的获取。通过建模实例和测量对比实验表明,方法具有操作简单、快速的特点,能满足较高的精度要求,具有一定的应用价值。     

建筑物侧面轮廓线约束的高分辨率遥感影像建筑物高度估算方法

针对现有利用阴影长度法提取建筑物高度时存在的阴影间相互遮挡问题，提出了一种基于建筑物侧面轮廓线进行建筑物高度估算的新方法。首先，利用RPC模型计算建筑物像点位移的方向与卫星成像角度，再将遥感影像进行旋转，使建筑物像点位移沿水平方向；然后，利用Canny算法进行轮廓检测，并构建一定长度的矩形形态学结构元素，对轮廓图像进行形态学开运算，以提取侧面轮廓线，再利用Hough变换与建筑物角点约束，对所提取的轮廓线进一步筛选；最后，根据卫星侧视成像时建筑物高度与像点位移的几何关系进行建筑物的高度估算。利用实际的高分辨率卫星影像对本文方法进行了验证，并与阴影法估算建筑物高度进行了对比。试验结果证明，利用建筑物侧面轮廓线进行建筑物高度估算平均误差可以达到0.7 m，且实际精度优于使用阴影法进行建筑物高度估算。     

利用机载LiDAR数据和高分辨率图像提取复杂城区建筑物

在复杂城区内部通常存在大量的阴影,建筑物的屋顶也有多种类型,这使得利用高分辨率遥感图像自动提取建筑物变得困难。针对上述2个问题,提出了一种综合利用高分辨率图像与机载Li DAR数据的城市建筑物提取新方法。首先,对归一化植被指数(normalized difference vegetation index,NDVI)和Li DAR高度数据设定阈值得到初步的建筑物提取结果;然后,分别利用阴影区NDVI、图像纹理和形态学滤波来改进结果;最后,采用局部的机载Li DAR数据和Quick Bird图像,对提出的方法进行验证,并与现有方法进行比较。研究结果表明,该方法可有效减少由阴影和不同屋顶特征所造成的错误识别,显著提高了建筑物提取精度。     

基于资源三号影像的建筑物高度信息提取方法

建筑物高度信息的获取是高分辨率遥感影像信息提取研究中的热点问题之一。该文提出了一套结合面向对象分类方法的建筑物高度反演技术方法。首先,利用建筑物和阴影的形态学指数,通过面向对象分类方法提取建筑物轮廓和阴影信息;然后,采用相交线平均法计算阴影长度;最后,根据阴影长度和建筑物高度的几何关系模型计算建筑物高度。采用西安市的国产资源三号(ZY-3)卫星遥感数据进行提取试验,通过171栋建筑物的实际测量高度对结果进行验证,获得了91.23%的总体精度,显示出该方法在建筑物高度信息提取研究方面具有一定的现实意义。     

高分辨率SAR图像中建筑物特征融合检测算法

针对高分辨率SAR图像中的建筑物检测问题,提出了一种基于视觉注意和特征融合的检测算法。首先,根据SAR图像中建筑物目标与背景存在较大差异的特点,采用视觉注意机制进行建筑物的感兴趣区分割;然后,提取位于感兴趣区域内的高亮线条和阴影区域;最后,采用D-S证据理论对注意焦点、高亮线条和阴影区域进行特征融合,实现建筑物目标的检测。实验结果表明,本文算法对矩形建筑物具有较高的检测精度。     

高分影像建筑物阴影优化提取与高度估算

针对现有方法提取阴影效率慢,提取不完整,估算过程未能实现半自动化甚至自动化的问题,该文基于高分二号影像,提出一种将K-means图像分割算法与阴影后处理结合一体应用在建筑物阴影提取的方法:首先,选择建筑物间隔稀疏,结构规则的城郊区域,利用K-means图像分割获取建筑物阴影、建筑物2类以提取建筑物阴影;其次,通过形态学算法、Canny边缘检测等对阴影后期处理,去除小区域及孔洞填充,边缘信息检测,获取最终建筑物阴影;最后,根据太阳、卫星、建筑物以及阴影长度之间几何关系计算建筑物高度。考虑研究区域户型,每层楼高以2.8 m量测建筑物实际高度作为验证,实验结果表明:利用K-means图像分割能有效提取出阴影区域,与后期阴影优化策略结合,大幅度改善了阴影区域的完整性,获取建筑物高度信息自动化程度得到提高。     

资源三号卫星前视影像的建筑物高度提取

建筑物高度对建筑物容积率、城市风向以及城市环境等都具有明显的影响。针对太阳入射方向与卫星观测方向在建筑物异侧时,建筑物侧面与阴影在遥感影像上因极其相似而难以区分的问题,该文基于资源三号卫星前视影像,利用基于规则的面向对象特征提取方法提取建筑物侧面及阴影特征。根据卫星成像时的太阳、卫星以及建筑物之间的空间几何关系,构建了建筑物侧面与阴影的长度比例系数,进而估算了建筑物的高度信息。最后以实测高度进行了高度提取的精度评价,验证结果表明,反演的平均精度达到了92.28%,证明了资源三号卫星前视影像在提取建筑物高度方面的良好可行性。     

结合形态学和光谱特征参数的建筑物阴影提取及其高度估算

建筑物高度信息对城市景观规划设计、城市人居和生态环境评估等都具有重要作用。针对当前卫星遥感影像提取建筑物高度研究中存在精度较低、人工干预程度过多的问题,本文基于GF-2卫星遥感影像数据,综合建筑物阴影指数(EMSI)、归一化植被指数(NDVI)和地表反射率等3个参数,实现了建筑物阴影的自动化提取,进而依据空间几何学关系,实现了建筑物高度的快速估算,最后对研究结果进行了实地检验。研究表明,利用建筑物阴影指数、NDVI、地表反射率等3个参数可有效提取建筑物阴影,且利用建筑物阴影进行建筑物高度估算的平均精度达到了95.40%,证明了高分二号卫星影像在提取建筑物高度方面具有较高的可行性。     

高分辨率遥感影像建筑容积率提取方法研究

提出了一种基于建筑物阴影的高分辨率卫星遥感影像建筑物容积率提取方法。首先利用高分辨率遥感影像提取城市大范围建成区建筑物阴影,再通过阴影矢量化、阴影坐标转换,将大比例尺的卫星分幅图进行自动拼接,最终根据阴影与建筑物面积关系回归分析、建筑物朝向分析等进行建筑容积率的计算和半自动提取。对上海中心城区的建筑容积率的提取实验验证了所提方法的有效性。     

高分辨率SAR和可见光图像同名点自动匹配技术

提出一种高分辨率SAR图像与光学图像之间的自动配准方法,首先利用仿射变换进行SAR图像和光学图像粗匹配,简化了整体算法的处理复杂度;然后使用边缘检测算子分别对光学图像和SAR图像进行特征提取,为后续精匹配做好了数据准备;最后基于归一化互相关原理进行图像之间的同名点精匹配。引入的比值边缘检测算子(ROA)与传统光学检测算子相比,可以较好地克服SAR斑点噪声,提取出的SAR特征图像与光学特征图像非常相似,提高了算法的准确性和鲁棒性。以星载3米TerraSar数据和1米SPOT数据为例进行算法验证,实验结果表明该算法能实现自动的异源图像之间的像素级匹配。     

利用后向散射特性从高分辨率SAR影像中提取建筑物高度

研究了一种基于雷达后向散射特性,从单张星载高分辨率SAR影像提取建筑物高度的方法。该方法通过检测建筑物二次散射强度,同时获取目标方位角、入射角等参数以及相关介质特性等信息,根据后向散射几何光学模型估算建筑物高度。实验验证了对于相当一部分典型建筑物都能够获得较为理想的高程估计值,进而证明了该方法提取建筑物高度的可行性和有效性。     

<Emphasis Type="Italic">3D</Emphasis> Modelling of Urban Area Using Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a newly-developed remote sensing technology that works in all weather and independent of daylight. Recent satellite designs such as TerraSAR-x, which have resolutions of a couple of meters and sub-meters, have provided appropriate data for modelling and monitoring of urban areas. Image classification and height information extraction is possible considering the nature of SAR data. In this paper, a proper classification method for high-resolution SAR images has been used in urban areas. This classifier is based on statistical models. First, statistical models that are well adapted to urban SAR images are selected. Initial labelling is performed using the maximum likelihood method. A method based on Markov random fields is applied to improve the results by considering neighbourhood information. Meanwhile, topographic information is extracted using the phase difference obtained from SAR interferometry. After classification and height extraction, the homogeneous regions consisting of locations with similar objects are determined. The homogeneous region adjacency graph are generated using vectors containing classification information, extracted objects, height of pixels forming each region, and information on the neighbouring areas. Height and classification information are then merged by assigning height conditions based on the nature of objects and optimizing an energy function. The results obtained, including buildings, streets, and corner reflectors, are easily recognizable. The overall accuracy is improved from 57% in the initial classification to 95% in the employed procedure. Moreover, the accuracy of height estimation is about 2.74 m, which is acceptable for height estimations of buildings with more than one floor.     

Stereo analysis of high-resolution SAR images for building height estimation in cases of orthogonal aspect directions

SAR stereo image analysis for 3D information extraction is mostly carried out based on imagery taken under same-side or opposite-side viewing conditions. For urban scenes in practice stereo is up to now usually restricted to the first configuration, because increasing image dissimilarity connected with rising illumination direction differences leads to a lack of suitable features for matching, especially in the case of low or medium resolution data. However, due to two developments SAR stereo from arbitrary viewing conditions becomes an interesting option for urban information extraction. The first one is the availability of airborne sensor systems, which are capable of more flexible data acquisition in comparison to satellite sensors. This flexibility enables multi-aspect analysis of objects in built-up areas for various kinds of purpose, such as building recognition, road network extraction, or traffic monitoring. The second development is the significant improvement of the geometric resolution providing a high level of detail especially of roof features, which can be observed from a wide span of viewpoints. In this paper, high-resolution SAR images of an urban scene are analyzed in order to infer buildings and their height from the different layover effects in views taken from orthogonal aspect angles. High level object matching is proposed that relies on symbolic data, representing suitable features of urban objects. Here, a knowledge-based approach is applied, which is realized by a production system that codes a set of suitable principles of perceptual grouping in its production rules. The images are analyzed separately for the presence of certain object groups and their characteristics frequently appearing on buildings, such as salient rows of point targets, rectangular structures or symmetries. The stereo analysis is then accomplished by means of productions that combine and match these 2D image objects and infer their height by 3D clustering. The approach is tested using real SAR data of an urban scene.     

一种从SAR图像中提取城市道路网络的方法

提出一种从高分辨率SAR图像中提取城市道路网络的算法.在高分辨率SAR图像中,道路在空间结构上表现为一细长的且宽度基本恒定不变的均匀区域.利用模糊C均值聚类方法对高分辨率SAR图像进行聚类分析,将道路类像素从原始图像中分离出来.为突出道路形状特征,减少冗余信息,对聚类结果进行细化,同时利用跟踪算子消除短线段;以提取道路中心线二值图的像素值作为图像能量,应用Snakes模型检测道路网络.通过实际SAR图像验证,该算法可以准确提取复杂的城市道路网络.     

基于RPC的TerraSAR-X影像立体定向平差模型

针对新型高分辨率雷达卫星TerraSAR-X立体像对,本文提出采用基于RPC的平差模型,通过少量的地面控制点来拟合因传感器不稳定、平台星历数据不精确及测距误差引起的影像几何畸变,从而达到精确定向目的.为验证RPC平差模型的适用性,通过在立体成像区域均匀市设人工角反射器点的方法验证其模型精度,并评估了其三维定向平差后的精...     

Extracting urban water bodies from high-resolution radar images: Measuring the urban surface morphology to control for radar’s double-bounce effect

Satellite remote sensing is an effective method for extracting water bodies on a large scale. Radar imagery, such as synthetic aperture radar (SAR) imagery, can penetrate clouds and provide opportunities for water body identification when in situ observations are difficult to obtain because of severe weather conditions. However, when using SAR images in urban areas to extract water bodies, the radar’s double-bounce effect results in complicated backscatter patterns of water near urban features such as buildings due to the side-looking properties of SAR sensors and the vertical urban structures. Therefore, the objective of this study is to propose a reliable urban water extraction framework for SAR images that integrates urban surface morphological features for controlling radar’s multiple bounces. Statistical (logistic regression) and machine-learning (random forest) models were used to explore how radar’s double-bounce effect influences the prediction performance of urban water extraction. Our findings indicate that when extracting urban water bodies, urban water’s backscatter values could be significantly interfered by the neighboring building density above a threshold height that contributes to radar’s multiple bounces. Without model calibration, our framework incorporating urban surface morphology demonstrates high prediction ability with an Area Under the Curve (AUC) of 0.914 and with 97.0% of urban water cells correctly identified by testing in another city sharing similar urban forms. In summary, our study provides a better understanding of the role of the urban surface morphology in the double-bounce effect in SAR images, specifically for differentiating urban water and land, thereby improving the accuracy of urban water extraction and enhancing the feasibility of further applications of SAR imagery under complex urban landscapes.     

高分辨率遥感影像建筑物分级提取

高分辨率遥感影像建筑物信息自动提取是遥感应用研究中的一个热点问题,但由于受到成像条件不同、背景地物复杂、建筑物类型多样等多个因素的影响使得建筑物的自动提取仍然十分困难。为此,在综合考虑影像光谱、几何与上下文特征的基础上,提出了一种基于面向对象与形态学相结合的高分辨率遥感影像建筑物信息分级提取方法。该方法首先利用影像的多尺度及多方向Gabor小波变换结果提取建筑物特征点;然后采用面向对象的思想构建空间投票矩阵来度量每一个像素点属于建筑物区域的概率,从而提取出建筑物区域边界;最后在提取的建筑物区域内应用形态学建筑物指数实现建筑物信息的自动提取。实验结果表明,本文方法能够高效、高精度地完成复杂场景下的建筑物信息提取,且提取结果的正确性和完整性都优于效果较好的PanTex算法。     

An Adaptive and Iterative Method of Urban Area Extraction From SAR Images

This letter presents a new method for unsupervised urban area extraction from synthetic aperture radar (SAR) images based on the ffmax algorithm proposed by C. Gouinaud specially for acquiring urban areas in SPOT imagery. According to the statistical characteristics of urban areas, an adaptive and iterative method based on the low-level extraction given by the ffmax algorithm using a large window is proposed. Experimental results on real SAR images show that the proposed automatic method works quickly and can preserve the borders of urban areas as well as avoid the disturbance of other classes and the extractions of urban areas are reliable and precise     

一种高分辨率SAR影像道路提取方法

针对高分辨率SAR影像中细节信息损坏道路的面特征结构、影响道路提取,基于影像统计特征,给出一种结合区域生长和细节信息识别的道路提取方法。该方法通过区域生长提取呈现面特征的暗目标(道路框架),利用CFAR算法识别细节,通过形态学融合得到最终结果。为降低高分辨率影像区域异质性对提取结果影响,提出了一种自适应CFAR算法,相比之前算法可自适应删除干扰点;并引入有效表征影像统计的GA0分布。利用海南省陵水黎族自治县机载X波段高分辨率SAR数据的幅度影像进行实验,结果表明,该方法能有效提取呈面特征的道路,获得准确的道路宽度和中心线信息。     

Improving urban road extraction in high-resolution images exploiting directional filtering, perceptual grouping, and simple topological concepts

In this letter, the problem of detecting urban road networks from high-resolution optical/synthetic aperture radar (SAR) images is addressed. To this end, this letter exploits a priori knowledge about road direction distribution in urban areas. In particular, this letter presents an adaptive filtering procedure able to capture the predominant directions of these roads and enhance the extraction results. After road element extraction, to both discard redundant segments and avoid gaps, a special perceptual grouping algorithm is devised, exploiting colinearity as well as proximity concepts. Finally, the road network topology is considered, checking for road intersections and regularizing the overall patterns using these focal points. The proposed procedure was tested on a pair of very high resolution images, one from an optical sensor and one from a SAR sensor. The experiments show an increase in both the completeness and the quality indexes for the extracted road network.