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：多时相遥感数据中变化检测在监测灾害、森林砍伐和城市规划等现实生活多个方面发挥着关键作用。在城市规划背景下，识别新建和拆除建筑物对于帮助景观和城市管理者促进可持续发展至关重要。虽然机载LiDAR点云在城市变化检测中已得到广泛应用，但最常见的方法需要将点云转换为规则网格的插值高程测量值（即数字高程模型）。然而，数字高程模型的插值步骤导致与物体高度相关的信息损失，影响建筑物变化检测能力，而LiDAR点云在第三维度的高分辨率恰恰对此最为有利。尽管近年来有研究尝试直接在点云上使用基于距离的计算方法或语义分割预处理步骤进行变化检测，但只有基于M3C2距离计算的方法能同时识别正向变化和负向变化，这对城市规划至关重要。基于前述论证，我们引入了一种基于最优传输的原则性变化检测流程，能够区分新建建筑物（正向变化）和拆除建筑物（负向变化）。在本工作中，我们提出使用非平衡最优传输来处理与双时相LiDAR点云中建筑物变化相关的质量创建与消解问题。我们在唯一公开的机载LiDAR变化检测数据集上展示了方法的有效性，通过显示其性能优于M3C2及Nicolas Courty等人在IGARSS 2016提出的基于最优传输的方法。