The tilted viewing nature of the off-nadir aerial images brings severe challenges to the building change detection (BCD) problem: the mismatch of the nearby buildings and the semantic ambiguity of the building facades. To tackle these challenges, we present a multi-task guided change detection network model, named as MTGCD-Net. The proposed model approaches the specific BCD problem by designing three auxiliary tasks, including: (1) a pixel-wise classification task to predict the roofs and facades of buildings; (2) an auxiliary task for learning the roof-to-footprint offsets of each building to account for the misalignment between building roof instances; and (3) an auxiliary task for learning the identical roof matching flow between bi-temporal aerial images to tackle the building roof mismatch problem. These auxiliary tasks provide indispensable and complementary building parsing and matching information. The predictions of the auxiliary tasks are finally fused to the main building change detection branch with a multi-modal distillation module. To train and test models for the BCD problem with off-nadir aerial images, we create a new benchmark dataset, named BANDON. Extensive experiments demonstrate that our model achieves superior performance over the previous state-of-the-art competitors.
翻译:远纳迪尔空中图像的偏斜视觉性质给建筑变化探测(BCD)问题带来了严重挑战:附近建筑物的不匹配和建筑物外形的语义模糊性。为了应对这些挑战,我们提出了一个多任务引导变化探测网络模型,称为MTGCD-Net。拟议模型设计了三项辅助任务,包括:(1) 预测楼顶和建筑物外形的比素明智的分类任务;(2) 学习每座建筑物屋顶到脚的顶部冲印抵消的辅助任务,以说明建筑楼顶的不匹配情况;(3) 学习双时空航空图像之间相同的屋顶匹配流动的辅助任务,以解决建筑屋顶不匹配问题。这些辅助任务提供了不可或缺的和互补的建筑分割和匹配信息。辅助任务的预测最终与主要建筑变化探测部门结合,并配有一个多模式的蒸馏模块。用离子航空图像对BCD问题进行培训和测试模型,我们创建了一个新的基准数据测试,即BANDON。我们以前实现的高级测试。