论文标题
连续域概括的激活回归与作物分类的应用
Activation Regression for Continuous Domain Generalization with Applications to Crop Classification
论文作者
论文摘要
卫星图像中的地理差异会影响机器学习模型推广到新区域的能力。在本文中，我们将中分辨率Landsat-8卫星图像中的地理概括建模为连续的域适应性问题，以证明模型如何通过适当的域知识更好地推广。我们开发了一个空间分布在整个美国大陆上的数据集，从而为地理对多光谱和时间分布的卫星图像中农作物分类的影响提供了宏观的见解。我们的方法证明了从1）通过地理位置相关的气候变量以及卫星数据转换为变压器模型的提高通用性，并在模型特征上回归以重建这些域变量。结合在一起，我们提供了关于卫星图像中的地理概括的新观点，以及一种简单的方法来利用领域知识。代码可在：\ url {https://github.com/samar-khanna/cropmap}中获得。
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem, demonstrating how models generalise better with appropriate domain knowledge. We develop a dataset spatially distributed across the entire continental United States, providing macroscopic insight into the effects of geography on crop classification in multi-spectral and temporally distributed satellite imagery. Our method demonstrates improved generalisability from 1) passing geographically correlated climate variables along with the satellite data to a Transformer model and 2) regressing on the model features to reconstruct these domain variables. Combined, we provide a novel perspective on geographic generalisation in satellite imagery and a simple-yet-effective approach to leverage domain knowledge. Code is available at: \url{https://github.com/samar-khanna/cropmap}
