Detecting complex building shapes in panchromatic satellite images for digital elevation model enhancement
Detecting complex building shapes in panchromatic satellite images for digital elevation model enhancement
复制标题
检测全色卫星图像中的复杂建筑形状以增强数字高程模型
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
P. Reinartz
中科院分区：
文献类型：
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作者：
B. Sirmaçek;P. d’Angelo;P. Reinartz
由于遥感领域提供了新的传感器和技术来积累城市区域的数据，这些区域的三维表示受到了各种应用的关注。三维城市区域表示可以用于详细的城市监测、变化和损伤检测目的。为了获得三维图像，最简单和最便宜的方法之一是使用数字高程模型（dem），该模型是利用立体视觉技术从非常高分辨率的立体卫星图像中生成的。遗憾的是，在应用DEM生成过程后，我们不能直接获得三维城市区域表示。在仅使用一组立体图像对生成的DEM中，建筑物墙体位置的噪声、匹配误差和不确定性通常非常高。这些不良影响增加了三维表现的复杂性。因此，自动DEM增强是一个开放且具有挑战性的问题。为了增强DEM，本文提出了一种基于建筑物形状检测的方法。我们使用DEM和M¨unchen的正校正全色Ikonos图像来解释我们的方法。在对DEM和Ikonos图像进行预处理后，我们对DEM进行局部阈值分割，以检测建筑物等高层城市物体的近似位置。为了检测复杂的建筑形状，我们发展了之前的矩形形状检测（盒拟合）算法。不幸的是，我们研究区域的建筑形状非常复杂。我们假设这些复杂建筑的形状可以通过拟合像链一样的小矩形来检测。因此，我们将检测到的建筑划分为细长的子部分。然后，我们将之前的矩形形状检测算法应用于这些子部件。在形状检测中，我们考虑Ikonos图像的Canny边缘来拟合矩形框。在合并所有检测到的矩形后，我们检测到甚至非常复杂的建筑结构的形状。最后，利用检测到的建筑物形状，在DEM中细化建筑物边缘，平滑建筑物屋顶上的噪声。我们相信，实施的增强不仅可以提供更好的视觉三维城市区域表现，还可以进行详细的变化和损坏调查。
Since remote sensing field provides new sensors and techniques to accumulate data on urban region, three-dimensional representation of these regions gained much interest for various applications. Three-dimensional urban region representation can be used for detailed urban monitoring, change and damage detection purposes. In order to obtain three-dimensional representation, one of the easiest and cheapest way is to use Digital Elevation Models (DEMs) which are generated from very high resolution stereo satellite images using stereovision techniques. Unfortunately after applying the DEM generation process, we can not directly obtain three-dimensional urban region representation. In the DEM which is generated using only one stereo image pairs, generally noise, matching errors, and uncertainty on building wall locations are very high. These undesirable effects increase the complexity in the three-dimensional representation. Therefore, automatic DEM enhancement is an open and challenging problem. In order to enhance DEM, herein we propose an approach based on building shape detection. We use DEM and orthorectified panchromatic Ikonos images of M¨ unchen to explain our method. After applying pre-processing to both DEM and Ikonos image, we apply local thresholding to DEM to detect approximate locations of high urban objects like buildings. In order to detect complex building shapes, we develop our previous rectangular shape detection (box-fitting) algorithm. Unfortunately, building shapes are very complex in our study region. We assume that shapes of these complex buildings can be detected by fitting small rectangles like a chain. Therefore, we divide detected buildings into elongated subparts. Then, we apply our previous rectangular shape detection algorithm to these subparts. In shape detection, we consider Canny edges of Ikonos image to fit rectangular boxes. After merging all detected rectangles, we detect shapes of even very complex building structures. Finally, using detected building shapes, we refine building edges in the DEM and smooth the noise on building rooftops. We believe that the implemented enhancement will not only provide better visual three-dimensional urban region representation, but also will lead to detailed change and damage investigations.