用时空线索提升弱监督遥感图像分割，仅凭类别标签就能达到接近全监督效果。
Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation
- 从空间和时间两方面提取作物特征线索，增强模型对关键区域的感知能力。
- 在多个遥感数据集上，弱监督模型性能达到全监督的95%。
- 适合农业遥感、土地利用分析等需要低成本标注的场景。
通过卫星图像时序序列（SITS）实现自动化作物制图已成为农业监测与管理的关键方向。然而，由于分辨率低且地块边界模糊，对像素级掩码进行标注极为复杂耗时。本文采用弱监督范式（仅提供图像级类别标签），以减轻标注负担。针对SITS的独特挑战——空间邻域噪声干扰及异常时相带来的语义偏差，提出一种新方法Exact：首先引入一组空间线索，显式捕捉不同作物在最具类别区分性的区域中的代表性模式；同时利用模型的时间-类别交互机制，强化关键时序片段的贡献，提升对作物区域的感知能力。基于提取的时空线索生成基于线索的CAMs，用于有效监督SITS分割网络。实验表明，该方法在多个SITS基准测试中表现优异，使用Exact生成的掩码训练的分割网络性能达到全监督版本的95%，充分展示了弱监督范式在作物制图中的巨大潜力。代码将公开。
原文摘要 · Abstract (English)
Automated crop mapping through Satellite Image Time Series (SITS) has emerged as a crucial avenue for agricultural monitoring and management. However, due to the low resolution and unclear parcel boundaries, annotating pixel-level masks is exceptionally complex and time-consuming in SITS. This paper embraces the weakly supervised paradigm (i.e., only image-level categories available) to liberate the crop mapping task from the exhaustive annotation burden. The unique characteristics of SITS give rise to several challenges in weakly supervised learning: (1) noise perturbation from spatially neighboring regions, and (2) erroneous semantic bias from anomalous temporal periods. To address the above difficulties, we propose a novel method, termed exploring space-time perceptive clues (Exact). First, we introduce a set of spatial clues to explicitly capture the representative patterns of different crops from the most class-relative regions. Besides, we leverage the temporal-to-class interaction of the model to emphasize the contributions of pivotal clips, thereby enhancing the model perception for crop regions. Build upon the space-time perceptive clues, we derive the clue-based CAMs to effectively supervise the SITS segmentation network. Our method demonstrates impressive performance on various SITS benchmarks. Remarkably, the segmentation network trained on Exact-generated masks achieves 95% of its fully supervised performance, showing the bright promise of weakly supervised paradigm in crop mapping scenario. Our code will be publicly available.
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