标题： TEA：时间自适应卫星图像语义分割 显示英文标题

标题： TEA: Temporal Adaptive Satellite Image Semantic Segmentation

摘要： 基于卫星图像时序（SITS）的作物制图在农业生产环境中具有重要的经济价值，其中地块分割是关键步骤。 现有方法在具有预定序列长度的SITS分割中取得了显著进展。 然而，我们发现这些方法忽略了模型在不同时间长度场景中的泛化能力，导致在这些情况下分割结果明显较差。 为了解决这个问题，我们提出了TEA，一种TEmporal Adaptive SITS语义分割方法，以增强模型在不同序列长度下的鲁棒性。 我们引入了一个教师模型，该模型封装了全局序列知识，以指导具有自适应时间输入长度的学生模型。 具体来说，教师通过中间嵌入、原型和软标签视角来塑造学生特征空间，以实现知识迁移，同时动态聚合学生模型以减轻知识遗忘。 最后，我们引入了全序列重建作为辅助任务，以进一步提高不同时间长度输入的表示质量。 通过大量实验，我们证明了我们的方法在常见基准上对不同时间长度的输入带来了显著改进。 我们的代码将公开可用。 显示英文摘要

摘要： Crop mapping based on satellite images time-series (SITS) holds substantial economic value in agricultural production settings, in which parcel segmentation is an essential step. Existing approaches have achieved notable advancements in SITS segmentation with predetermined sequence lengths. However, we found that these approaches overlooked the generalization capability of models across scenarios with varying temporal length, leading to markedly poor segmentation results in such cases. To address this issue, we propose TEA, a TEmporal Adaptive SITS semantic segmentation method to enhance the model's resilience under varying sequence lengths. We introduce a teacher model that encapsulates the global sequence knowledge to guide a student model with adaptive temporal input lengths. Specifically, teacher shapes the student's feature space via intermediate embedding, prototypes and soft label perspectives to realize knowledge transfer, while dynamically aggregating student model to mitigate knowledge forgetting. Finally, we introduce full-sequence reconstruction as an auxiliary task to further enhance the quality of representations across inputs of varying temporal lengths. Through extensive experiments, we demonstrate that our method brings remarkable improvements across inputs of different temporal lengths on common benchmarks. Our code will be publicly available.