Abstract
We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning. Extending previous research, we followed a selective alignment process to identify similar images in the two geographical domains and then tested an array of data-based unsupervised domain adaptation approaches in a zero-shot setting as well as with a small amount of fine-tuning. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting, and even more so with a small amount of fine-tuning. These methods outperform both an untransformed baseline and a popular data-based image-to-image translation model. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively.
Abstract (translated)
我们探讨了简单的方法来适应训练好的多任务UNet，该模型通过遥感数据在没有训练领域自适应分类器和大量微调的情况下预测树冠覆盖度和高度，以预测新的地理设置。扩展前人研究，我们采用选择性对齐过程来识别两个地理域中的相似图像，然后在一击零数据的情况下以及少量微调的情况下测试了一组数据自适应域迁移方法。我们发现，基于数据的图像匹配方法在零击零数据的情况下产生了积极的结果，而且甚至更好的结果是在少量微调的情况下。这些方法优于未转换的基线和一种流行的基于数据的照片-图像转换模型。在树冠覆盖度和高度任务上，最佳表现的方法是像素分布适应和傅里叶域适应。
URL
https://arxiv.org/abs/2404.10626
