Precise and timely information about crop types plays a crucial role in various agriculture-related applications. However, crop type mapping methods often face significant challenges in cross-regional and cross-time scenarios with high discrepancies between temporal-spectral characteristics of crops from different regions and years. Unsupervised domain adaptation (UDA) methods have been employed to mitigate the problem of domain shift between the source and target domains. Since these methods require source domain data during the adaptation phase, they demand significant computational resources and data storage, especially when large labeled crop mapping source datasets are available. This leads to increased energy consumption and financial costs. To address this limitation, we developed a source-free UDA method for cross-regional and cross-time crop mapping, capable of adapting the source-pretrained models to the target datasets without requiring the source datasets. The method mitigates the domain shift problem by leveraging mutual information loss. The diversity and discriminability terms in the loss function are balanced through a novel unsupervised weighting strategy based on mean confidence scores of the predicted categories. Our experiments on mapping corn, soybean, and the class Other from Landsat image time series in the U.S. demonstrated that the adapted models using different backbone networks outperformed their non-adapted counterparts. With CNN, Transformer, and LSTM backbone networks, our adaptation method increased the macro F1 scores by 12.9%, 7.1%, and 5.8% on average in cross-time tests and by 20.1%, 12.5%, and 8.8% on average in cross-regional tests, respectively. Additionally, in an experiment covering a large study area of 450 km 300 km, the adapted model with the CNN backbone network obtained a macro F1 score of 92.6%, outperforming its non-adapted counterpart with a macro F1 score of 89.2%. Our experiments on mapping the same classes using Sentinel-2 image times series in France demonstrated the effectiveness of our method across different countries and sensors. We also tested our method in more diverse agricultural areas in Denmark and France containing six classes. The results showed that the adapted models outperformed the non-adapted models. Moreover, in within-season experiments, the adapted models performed better than the non-adapted models in the vast majority of weeks. These results and their comparison to those obtained by the other investigated UDA methods demonstrated the efficiency of our proposed method for both end-of-season and within-season crop mapping tasks. Additionally, our study showed that the method is modular and flexible in employing various backbone networks. The code and data are available at .

中文翻译：

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一种基于卫星图像时间序列的跨区域、跨时间作物制图的无源无监督域自适应方法


有关作物类型的准确及时的信息在各种农业相关应用中发挥着至关重要的作用。然而，作物类型制图方法往往在跨区域和跨时间场景中面临重大挑战，不同地区和年份作物的时间光谱特征之间存在很大差异。无监督域适应（UDA）方法已被用来缓解源域和目标域之间的域转移问题。由于这些方法在适应阶段需要源域数据，因此它们需要大量的计算资源和数据存储，特别是当大型标记作物映射源数据集可用时。这导致能源消耗和财务成本增加。为了解决这个限制，我们开发了一种用于跨区域和跨时间作物映射的无源 UDA 方法，能够使源预训练模型适应目标数据集，而不需要源数据集。该方法通过利用互信息损失来缓解域转移问题。损失函数中的多样性和可辨别性项通过基于预测类别的平均置信度得分的新型无监督加权策略来平衡。我们对美国陆地卫星图像时间序列中的玉米、大豆和其他类进行绘图的实验表明，使用不同骨干网络的适应模型优于未适应的模型。借助 CNN、Transformer 和 LSTM 主干网络，我们的适应方法在跨时间测试中将宏 F1 分数平均提高了 12.9%、7.1% 和 5.8%，在跨时间测试中平均提高了 20.1%、12.5% 和 8.8% - 分别进行区域测试。 此外，在覆盖450 km×300 km的大型研究区域的实验中，采用CNN骨干网络的适应模型获得了92.6％的宏观F1分数，优于未适应模型的89.2％的宏观F1分数。我们在法国使用 Sentinel-2 图像时间序列绘制相同类别的实验证明了我们的方法在不同国家和传感器上的有效性。我们还在丹麦和法国更多样化的农业地区测试了我们的方法，其中包含六个类别。结果表明，适应模型优于未适应模型。此外，在季节内实验中，适应模型在绝大多数周内都比未适应模型表现更好。这些结果以及与其他研究的 UDA 方法获得的结果的比较证明了我们提出的方法对于季末和季内作物绘图任务的效率。此外，我们的研究表明，该方法是模块化的，并且可以灵活地使用各种骨干网络。代码和数据可在 处获得。