用Sentinel-1数据生成NDWI，解决云遮挡问题
A light-weight model to generate NDWI from Sentinel-1
- 基于深度学习，从Sentinel-1雷达图像生成NDWI
- NDWI预测准确率达0.9134，AUC为0.8656
- 适合需全天候水体监测的场景
利用Sentinel-2影像计算归一化差异水体指数（NDWI）在水体范围检测等领域有广泛应用，但云层遮挡严重影响其效果。本文提出一种深度学习模型，可直接从Sentinel-1雷达影像生成NDWI，有效克服云层障碍。实验表明，该模型在预测NDWI时达到0.9134的高准确率和0.8656的AUC；在回归任务中R²得分为0.4984，在分割任务中平均交并比（Mean IoU）达0.4139。结果证明，该模型为从Sentinel-1生成NDWI提供了首个稳健方案，适用于云覆盖和夜间等复杂条件下的多种应用。
原文摘要 · Abstract (English)
The use of Sentinel-2 images to compute Normalized Difference Water Index (NDWI) has many applications, including water body area detection. However, cloud cover poses significant challenges in this regard, which hampers the effectiveness of Sentinel-2 images in this context. In this paper, we present a deep learning model that can generate NDWI given Sentinel-1 images, thereby overcoming this cloud barrier. We show the effectiveness of our model, where it demonstrates a high accuracy of 0.9134 and an AUC of 0.8656 to predict the NDWI. Additionally, we observe promising results with an R2 score of 0.4984 (for regressing the NDWI values) and a Mean IoU of 0.4139 (for the underlying segmentation task). In conclusion, our model offers a first and robust solution for generating NDWI images directly from Sentinel-1 images and subsequent use for various applications even under challenging conditions such as cloud cover and nighttime.
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