ABSTRACT

Thermography is an efficient way of detecting the thermal problems of the roof as a major part of a building’s energy dissipation. Thermal images have a low spatial resolution, making it a challenge to produce a three-dimensional thermal model using aerial images. This paper proposes a combination of thermal and visible point clouds to generate a higher-resolution thermal point cloud from roofs of buildings. For this purpose, after obtaining the internal orientation parameters through camera calibration, visible and thermal point clouds were generated and then registered to each other using ground control points. The roofs of buildings were then extracted from the visible point cloud in four steps. First ground points were removed using cloth simulation filter (CSF), and then vegetation points were eliminated by applying entropy and red-green-blue vegetation index (RGBVI). Geometric features and a segmentation step were considered to filter roofs from other parts. Finally, by combining visible and thermal point clouds, the generated point had a high spatial resolution along with thermal information. In the achieved results, the thermal camera calibration was performed with an accuracy of 0.31 pixels, and the thermal and visible point clouds were registered with an absolute distance of < 0.3 m. The experimental results showed an accuracy of 18 cm for automated extraction of building roofs and 0.6 pixel for production of a high-resolution thermal point cloud, which was five times the density of the primary thermal point cloud and free from distortions.

摘要

热成像是检测作为建筑物能量耗散主要部分的屋顶热问题的有效方法。热图像的空间分辨率较低，这使得使用航拍图像生成三维热模型成为一项挑战。本文提出了热点云和可见光点云的组合，以从建筑物屋顶生成更高分辨率的热点云。为此，在通过相机校准获得内部方向参数后，生成可见光点云和热点云，然后使用地面控制点相互配准。然后分四步从可见点云中提取建筑物的屋顶。使用布料模拟过滤器 (CSF) 去除第一个接地点，然后通过应用熵和红绿蓝植被指数（RGBVI）消除植被点。几何特征和分割步骤被认为是从其他部分过滤屋顶。最后，通过结合可见点云和热点云，生成的点具有高空间分辨率和热信息。在取得的结果中，热像仪标定的精度为 0.31 像素，热和可见光点云以 < 0.3 m 的绝对距离配准。实验结果表明，自动提取建筑物屋顶的精度为 18 厘米，生成高分辨率热点云的精度为 0.6 像素，是主要热点云密度的五倍，并且没有失真。几何特征和分割步骤被认为是从其他部分过滤屋顶。最后，通过结合可见点云和热点云，生成的点具有高空间分辨率和热信息。在取得的结果中，热像仪标定的精度为 0.31 像素，热和可见光点云以 < 0.3 m 的绝对距离进行配准。实验结果表明，自动提取建筑物屋顶的精度为 18 厘米，生成高分辨率热点云的精度为 0.6 像素，是主要热点云密度的五倍，并且没有失真。几何特征和分割步骤被认为是从其他部分过滤屋顶。最后，通过结合可见点云和热点云，生成的点具有高空间分辨率和热信息。在取得的结果中，热像仪标定的精度为 0.31 像素，热和可见光点云以 < 0.3 m 的绝对距离配准。实验结果表明，自动提取建筑物屋顶的精度为 18 厘米，生成高分辨率热点云的精度为 0.6 像素，是主要热点云密度的五倍，并且没有失真。生成的点具有高空间分辨率和热信息。在取得的结果中，热像仪标定的精度为 0.31 像素，热和可见光点云以 < 0.3 m 的绝对距离配准。实验结果表明，自动提取建筑物屋顶的精度为 18 厘米，生成高分辨率热点云的精度为 0.6 像素，是主要热点云密度的五倍，并且没有失真。生成的点具有高空间分辨率和热信息。在取得的结果中，热像仪标定的精度为 0.31 像素，热和可见光点云以 < 0.3 m 的绝对距离配准。实验结果表明，自动提取建筑物屋顶的精度为 18 厘米，生成高分辨率热点云的精度为 0.6 像素，是主要热点云密度的五倍，并且没有失真。