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首页> 外文期刊>Arabian Journal for Science and Engineering >Building Extraction from Stereo Aerial Imagery Using Dynamic Programming
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Building Extraction from Stereo Aerial Imagery Using Dynamic Programming

机译：使用动态编程，从立体声空中图像建立提取

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In this paper, a novel technique for building detection and extraction and simple building reconstruction from stereo aerialimagery is presented. This research hypothesises that geometric distortion in buildings will lead to occlusion at depth discontinuities.Depth discontinuities around buildings can be identified by determining the occlusion. Accordingly, the roofcan be distinguished from the ground. Occlusion usually occurs in the direction that is perpendicular or at the edge angled tothe baseline, and no occlusion occurs when the building edge is parallel to the baseline. Therefore, another stereo pair shouldbe used to detect the depth discontinuities around the object. Dynamic programming algorithm is implemented to detectoccluded pixels at the depth discontinuities. Accordingly, the occluded pixels can be identified in the form of a point cloud.The produced point cloud is scattered and cannot be used to identify or extract the building boundary. Therefore, the pointcloud is converted to a raster image for detecting buildings that are shown as blobs. The algorithm is later used to extract thebuilding shape and construction. The proposed technique is fully automated and does not require human interference. Thealgorithm is applied to two study areas. Two stereo pairs are used for the first study area, and only one stereo pair is availableand applied for the second study area. Analyses show that the correctness and completeness of object accuracy assessmentfor the first study area are 100% and 97%, respectively; those for the second study area are 83% and 75%.
机译：本文采用了一种基于立体天线的检测和提取和简单建筑重建的新技术提出了图像。这项研究假设建筑物中的几何变形将导致深度不连续性闭塞。可以通过确定闭塞来识别建筑物周围的深度不连续性。因此，屋顶可以与地面区分开。闭塞通常发生在垂直或边缘的方向上当建筑边缘平行于基线时，基线和没有闭塞发生。因此，另一个立体对应该用于检测对象周围的深度不连续性。实现动态编程算法以检测在深度不连续性的闭塞像素。因此，可以以点云的形式识别遮挡像素。产生的点云分散，不能用于识别或提取建筑边界。因此，这一点云被转换为栅格图像，以检测显示为Blobs的建筑物。算法后来用于提取建筑形状和施工。该技术完全自动化，不需要人为干扰。这算法应用于两个研究区域。第一个研究区域使用两个立体对，只有一个立体声对并申请第二学习区域。分析表明，对象精度评估的正确性和完整性对于第一学习区域，分别为100％和97％;第二学习面积的人数为83％和75％。

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