Abstract
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.
Abstract (translated)
基于卫星图像时间序列（SITS）的作物地图绘制在农业生产中具有重要的经济价值，其中地块分割是关键步骤之一。现有方法已经在预设的时间序列长度上实现了显著的进展。然而，我们发现这些方法忽略了模型在不同时间长度场景下的泛化能力，导致此类情况下的分割效果明显较差。为解决这一问题，我们提出了TEA（Temporal Adaptive SITS Semantic Segmentation），这是一种增强型时间适应性SITS语义分割方法，旨在提高模型在变化序列长度下工作的韧性。 我们的方法引入了一个教师模型，该模型封装了整个序列的知识，来指导一个可以自适应调整时间输入长度的学生模型。具体来说，通过中间嵌入、原型和软标签视角，教师模型塑造学生模型的特征空间以实现知识转移，并动态聚合学生模型以减轻知识遗忘问题。 最后，我们引入全序列重构作为辅助任务，进一步提升不同时间长度输入下的表示质量。通过广泛的实验，我们在常用基准数据集上展示了我们的方法在不同时间长度输入下取得了显著改进的效果。我们的代码将公开发布。
URL
https://arxiv.org/abs/2601.04956
