论文标题
用3D表面语义监督遥感变更检测模型
Supervising Remote Sensing Change Detection Models with 3D Surface Semantics
论文作者
论文摘要
遥感变更检测，确定同一位置的场景之间的变化是具有广泛应用的研究领域。多模式自我监管预处理的最新进展导致了最先进的方法，该方法超越了仅在光学图像上训练的视觉模型。在遥感领域，有大量重叠的2D和3D模式，可以利用这些模型中的表示模型中的表示形式。在本文中，我们提出了使用光学RGB和高于地面（AGL）地图对的对比度表面图像预处理（CSIP）。然后，我们在几个构建细分中评估了这些预算的模型，并更改检测数据集，以表明我们的方法实际上确实是提取与自然和人工表面信息相关的下游应用程序相关的提取功能。
Remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining have resulted in state-of-the-art methods which surpass vision models trained solely on optical imagery. In the remote sensing field, there is a wealth of overlapping 2D and 3D modalities which can be exploited to supervise representation learning in vision models. In this paper we propose Contrastive Surface-Image Pretraining (CSIP) for joint learning using optical RGB and above ground level (AGL) map pairs. We then evaluate these pretrained models on several building segmentation and change detection datasets to show that our method does, in fact, extract features relevant to downstream applications where natural and artificial surface information is relevant.
