用超分辨率技术提升卫星图精度，快速评估洪灾农田损毁情况
FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
- 基于深度学习，将10米分辨率卫星图升维至3米
- 在比哈尔邦数据集上，严重损毁分类F1值从0.83提升至0.89
- 适合灾后农业应急评估，尤其适用于低预算地区
洪水后政府救灾资源分配面临挑战。印度农作物常受洪水影响，因此快速准确地进行作物损毁评估对灾后农业管理至关重要。传统人工调查速度慢且存在偏差，现有基于卫星的方法受限于云层遮挡和空间分辨率不足。为此，本文提出FLNet，一种基于深度学习的新型架构，利用超分辨率技术将哨兵-2卫星图像的10米分辨率提升至3米，再进行损毁分类。我们在比哈尔邦洪灾农田数据集（BFCD-22）上测试该模型，结果显示严重损毁类别的F1分数从0.83提升至0.89，几乎达到商业高分辨率影像的0.89水平。该工作提供了一种成本可控、可扩展的解决方案，推动全国范围从人工向自动化、高保真损毁评估的转型。
原文摘要 · Abstract (English)
Distributing government relief efforts after a flood is challenging. In India, the crops are widely affected by floods; therefore, making rapid and accurate crop damage assessment is crucial for effective post-disaster agricultural management. Traditional manual surveys are slow and biased, while current satellite-based methods face challenges like cloud cover and low spatial resolution. Therefore, to bridge this gap, this paper introduced FLNet, a novel deep learning based architecture that used super-resolution to enhance the 10 m spatial resolution of Sentinel-2 satellite images into 3 m resolution before classifying damage. We tested our model on the Bihar Flood Impacted Croplands Dataset (BFCD-22), and the results showed an improved critical "Full Damage" F1-score from 0.83 to 0.89, nearly matching the 0.89 score of commercial high-resolution imagery. This work presented a cost-effective and scalable solution, paving the way for a nationwide shift from manual to automated, high-fidelity damage assessment.
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