The detection of changes occurring in multi-temporal remote sensing data plays a crucial role in monitoring several aspects of real life, such as disasters, deforestation, and urban planning. In the latter context, identifying both newly built and demolished buildings is essential to help landscape and city managers to promote sustainable development. While the use of airborne LiDAR point clouds has become widespread in urban change detection, the most common approaches require the transformation of a point cloud into a regular grid of interpolated height measurements, i.e. Digital Elevation Model (DEM). However, the DEM's interpolation step causes an information loss related to the height of the objects, affecting the detection capability of building changes, where the high resolution of LiDAR point clouds in the third dimension would be the most beneficial. Notwithstanding recent attempts to detect changes directly on point clouds using either a distance-based computation method or a semantic segmentation pre-processing step, only the M3C2 distance computation-based approach can identify both positive and negative changes, which is of paramount importance in urban planning. Motivated by the previous arguments, we introduce a principled change detection pipeline, based on optimal transport, capable of distinguishing between newly built buildings (positive changes) and demolished ones (negative changes). In this work, we propose to use unbalanced optimal transport to cope with the creation and destruction of mass related to building changes occurring in a bi-temporal pair of LiDAR point clouds. We demonstrate the efficacy of our approach on the only publicly available airborne LiDAR dataset for change detection by showing superior performance over the M3C2 and the previous optimal transport-based method presented by Nicolas Courty et al.at IGARSS 2016.
翻译:检测多时遥感数据的变化在监测实际生活的若干方面,例如灾害、毁林和城市规划方面发挥着关键作用。在后一种情况下,确定新建和拆除的建筑物对于帮助地貌景观和城市管理人员促进可持续发展至关重要。虽然在城市变化探测中,利用空气中的LIDAR点云已经变得广泛,但最常见的方法要求将点云转换成一个内插高度测量的常规网格,即数字升降模型(DEM)。然而,DEM的内插步骤造成与物体高度有关的信息丢失,影响对建筑变化的探测能力,而在这后一种情况下,发现新建的LIDAR点云云云对第三个方面最为有益。尽管最近试图利用远程计算法或处理前的语义分解来直接探测点云的变化,但只有基于M3C2的远距离计算方法才能确定正向和负向上的变化,这在城市规划中至关重要。根据先前的论点,我们引入了一种有原则性的变化,即基于最佳的ARAR的探测轨迹检测管道,在第三个维度的LD点上,高解点云云云云云云云云云云云将最为有益。3,能够对新构建的建筑加以区分,我们所建的造的轨道,我们所建的造的造的造的轨道, 以正的造的造的造的轨道,以新造的造的造的轨道,以正的轨道为正的建筑为正的轨道,以新造的造的造的造的造的造的造的造的造的造的轨道, 造的造的造的造的轨道,以新的轨道,从而的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造的造