Land cover classification and change detection are two important applications of remote sensing and Earth observation (EO) that have benefited greatly from the advances of deep learning. Convolutional and transformer-based U-net models are the state-of-the-art architectures for these tasks, and their performances have been boosted by an increased availability of large-scale annotated EO datasets. However, the influence of different visual characteristics of the input EO data on a model's predictions is not well understood. In this work we systematically examine model sensitivities with respect to several color- and texture-based distortions on the input EO data during inference, given models that have been trained without such distortions. We conduct experiments with multiple state-of-the-art segmentation networks for land cover classification and show that they are in general more sensitive to texture than to color distortions. Beyond revealing intriguing characteristics of widely used land cover classification models, our results can also be used to guide the development of more robust models within the EO domain.

翻译：土地覆盖分类和变化检测是遥感与地球观测（EO）领域的两项重要应用，深度学习的发展极大推动了其技术进步。基于卷积和Transformer架构的U-Net模型已成为这些任务的最先进架构，而大规模标注EO数据集的日益普及进一步提升了其性能。然而，输入EO数据的不同视觉特征对模型预测的影响尚不明确。本研究系统分析了在推理阶段对输入EO数据施加多种颜色和纹理失真时，模型在未受此类失真训练情况下的敏感性。我们采用多种最先进的土地覆盖分类分割网络进行实验，结果表明：总体而言，模型对纹理失真的敏感性高于颜色失真。本研究不仅揭示了广泛使用的土地覆盖分类模型的独特特性，其结论还可用于指导地球观测领域更鲁棒模型的开发。