Siddiqui Fasahat Ullah, Teng Shyh Wei, Awrangjeb Mohammad, Lu Guojun
Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
Faculty of Science and Technology, Federation University Australia, Churchill VIC 3842, Australia.
Sensors (Basel). 2016 Jul 19;16(7):1110. doi: 10.3390/s16071110.
Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.
现有的自动建筑物提取方法在提取尺寸较小且具有透明屋顶的建筑物时效果不佳。大面积阈值的应用会阻碍对小型建筑物的检测，而在生成建筑物掩膜时使用地面点会妨碍对透明建筑物的检测。此外，现有方法在复杂环境（如山区和高植被地区）中提取建筑物时使用了大量参数。然而，大量参数的经验性调整降低了建筑物提取方法的鲁棒性。本文提出了一种新颖的基于梯度的建筑物提取（GBE）方法来解决这些局限性。所提出的方法将光探测与测距（LiDAR）高度信息转换为强度图像，无需对点位高度进行插值，然后分析图像中的梯度信息。一般来说，建筑物屋顶平面沿屋顶平面坡度具有恒定的高度变化，而树木具有随机的高度变化。通过这样的分析，可以提取更大尺寸范围的具有透明或不透明屋顶的建筑物。此外，引入了一种局部颜色匹配方法作为后处理阶段来消除树木。我们所提出方法的这个阶段不需要任何手动设置，所有参数都根据数据自动设置。其他后处理阶段，包括方差、点密度和阴影消除，也用于验证提取的建筑物，其中使用的经验性设置参数相对较少。通过基于对象和像素的指标（完整性、正确性和质量）在两个基准数据集上对所提出的GBE方法的性能进行了评估。我们的实验结果表明，所提出的方法在消除树木、提取各种尺寸的建筑物以及提取有和没有透明屋顶的建筑物方面是有效的。与当前最先进的建筑物提取方法相比，所提出的方法在各种评估指标上均优于现有方法。
