摘要
为有效地从海量、带噪阵列InSAR空间点中检测建筑立面,该文提出一种基于结构先验的渐进式建筑立面检测算法。该文算法首先将初始阵列InSAR空间点投影至地面以生成与建筑立面相应的连通区域,然后通过结构先验的引导逐步在每个连通区域内检测潜在的线段,进而根据线段及其对应的空间点生成相应的建筑立面;在此过程中,当前连通区域对应线段的检测空间根据其相邻连通区域内已检测线段构造,有效保证了整体效率与可靠性。实验结果表明,该文算法可快速从海量、带噪阵列InSAR空间点中检测出较多的可靠建筑立面,较好地克服了传统多模型拟合算法效率低与可靠性差的缺点。
This study proposes a progressive building facade detection method based on structure priors to effectively detect building facades from massive array InSAR spatial points with noise.First,the proposed method projects the initial array of InSAR three-Dimensional(3D)points on the ground to produce connected regions that correspond to building facades and then progressively detects potential line segments in each connected region under the guidance of structure priors.Furthermore,the proposed method generates building facades according to the detected line segments and their corresponding 3D points.In this process,the line segment detection space of the current connected region is constructed based on line segments detected in its neighboring connected regions,thereby improving the overall efficiency and reliability of the current line segment detection.Experimental results confirm that the proposed method can efficiently produce more reliable building facades from a massive array of InSAR 3D points with noise,overcoming several difficulties(such as low efficiency and inferior reliability)encountered in traditional multi-model fitting methods.
作者
王伟
许华荣
魏含玉
董秋雷
WANG Wei;XU Huarong;WEI Hanyu;DONG Qiulei(School of Network Engineering,Zhoukou Normal University,Zhoukou 466001,China;School of Computer Information Engineering,Xiamen University of Technology,Xiamen 361024,China;College of Mathematics and Statistics,Zhoukou Normal University,Zhoukou 466001,China;Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《雷达学报（中英文）》
CSCD
北大核心
2022年第1期144-156,共13页
Journal of Radars
基金
国家自然科学基金(61991423,U1805264)
空间光电测量与感知实验室开放基金(502K0019118)
河南省科技攻关项目(212102310397)。
