Abstract
Image-level regression is an important task in Earth observation, where visual domain and label shifts are a core challenge hampering generalization. However, cross-domain regression with remote sensing data remains understudied due to the absence of suited datasets. We introduce a new dataset with aerial and satellite imagery in five countries with three forest-related regression tasks. To match real-world applicative interests, we compare methods through a restrictive setup where no prior on the target domain is available during training, and models are adapted with limited information during testing. Building on the assumption that ordered relationships generalize better, we propose manifold diffusion for regression as a strong baseline for transduction in low-data regimes. Our comparison highlights the comparative advantages of inductive and transductive methods in cross-domain regression.
Abstract (translated)
图像级回归是地球观测中的一个重要任务，因为视觉领域和标签转移是泛化的关键挑战。然而，由于缺乏合适的数据集，跨领域回归与遥感数据仍然是一个研究不足的领域。我们介绍了一个包含五个国家航空和卫星图像的新数据集，包括三个与森林相关回归任务。为了满足现实应用的需求，我们在没有目标领域先前知识的情况下进行比较，并在测试期间使用有限的信息来适应模型。基于一个假设，有序关系具有更好的泛化能力，我们在低数据集环境下提出多维扩散作为转换的基础。我们的比较突出了归纳方法和转换方法在跨领域回归中的比较优势。
URL
https://arxiv.org/abs/2405.00514
