用卫星影像时序数据自监督检测作物胁迫，少标注也能准。
STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series
- 基于3D自编码器，融合四种植被指数捕捉时空胁迫模式。
- 水胁迫精度97.98%，氮胁迫85.08%，综合胁迫83.47%。
- 适合农业监测，尤其标注数据少的场景。
早期准确检测作物胁迫对提升农业产量和保障全球粮食安全至关重要。然而，大规模标注作物胁迫数据集的构建极具挑战。为此，我们提出一种新型时空胁迫网络（STS-NET），基于自监督3D卷积自编码器（3D-CAE），利用高分辨率Planetscope影像获取的归一化差异植被指数（NDVI）、绿光归一化差异植被指数（GNDVI）、红边叶绿素指数（RECI）和归一化差异红边指数（NDRE）构建卫星影像时序数据（SITS）的时空特征。模型在我们自建的BSPT（Barnala空间-时间）数据集上训练，并在印度北方邦拉克希普尔-赫里（LK）地区一个2.5英亩试验田采集的一年期真实甘蔗数据集上评估。结果显示，水胁迫检测精度达97.98%，氮胁迫为85.08%，综合胁迫为83.47%。结果表明，STS-NET在极少依赖标注数据的前提下，能有效检测甘蔗作物胁迫。此外，该模型还可作为简单模型的鲁棒特征提取器。
原文摘要 · Abstract (English)
Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a large labeled crop stress dataset is a challenging task. To address this challenge, we introduce a novel spatial-temporal stress network (STS-NET), built on a self-supervised 3D-convolutional autoencoder (3D-CAE), designed to utilize Satellite Image Time Series (SITS) data for crop stress detection. STS-NET exploits four vegetation indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (GNDVI), Red-Edge Chlorophyll Index (RECI) and Normalized Difference Red-Edge Index (NDRE) obtained from high resolution Planetscope imagery to capture spatiotemporal stress patterns. The model is trained on our BSPT (Barnala Spatial-Temporal) dataset and evaluated on a real-world sugarcane dataset collected over a year from a 2.5-acre test plot located in Lakhimpur-Kheri (LK) district in Uttar Pradesh in India. STS-NET achieved a precision of 97. 98\% for water stress, 85.08\% for nitrogen stress, and 83.47\% for combined stress. The results demonstrate the potential of STS-NET in effectively detecting stress in sugarcane crops with minimal reliance on labeled data. Furthermore, STS-NET can serve as a robust feature extractor for simpler models.
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