用卫星影像时序无监督检测甜菜胁迫，可跨年直接使用。
Sugar-Beet Stress Detection using Satellite Image Time Series
- 用3D卷积自编码器+时间编码提取甜菜生长动态特征
- 在无标签数据上实现健康与胁迫田块的聚类分离
- 模型可跨年应用，适合农业监测场景
卫星影像时序（SITS）数据因其丰富的光谱和时间特性，在农业任务中表现优异。本研究针对甜菜田胁迫检测问题，提出一种完全无监督的方法。通过3D卷积自编码器从哨兵-2图像序列中提取有意义的特征，并结合按采集日期定制的时间编码，更好捕捉甜菜的生长动态。学习到的表示用于下游聚类任务，以区分胁迫与健康田块。该系统可直接应用于不同年份的数据，为甜菜胁迫检测提供实用且易获取的工具。
原文摘要 · Abstract (English)
Satellite Image Time Series (SITS) data has proven effective for agricultural tasks due to its rich spectral and temporal nature. In this study, we tackle the task of stress detection in sugar-beet fields using a fully unsupervised approach. We propose a 3D convolutional autoencoder model to extract meaningful features from Sentinel-2 image sequences, combined with acquisition-date-specific temporal encodings to better capture the growth dynamics of sugar-beets. The learned representations are used in a downstream clustering task to separate stressed from healthy fields. The resulting stress detection system can be directly applied to data from different years, offering a practical and accessible tool for stress detection in sugar-beets.
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