用风格迁移提升高分遥感图像估碳精度
Estimating forest carbon stocks from high-resolution remote sensing imagery by reducing domain shift with style transfer
- 通过风格迁移减少不同遥感影像间的域偏移
- 结合Swin Transformer实现全局特征提取，碳储量估计准确率显著提升
- 适合做森林碳汇监测的遥感研究人员参考
森林是陆地重要的碳库，其碳汇功能可有效降低大气中二氧化碳浓度，缓解气候变化。当前森林碳储量监测与评估趋势是融合地面采样数据与卫星遥感影像，实现大范围观测。然而现有方法仍需提高精度。本研究以中国云南省曲靖市会泽县为实验区，利用GF-1 WFV与Landsat TM影像，采用风格迁移方法，引入Swin Transformer通过注意力机制提取全局特征，将碳储量估算问题转化为图像翻译任务，有效缓解了多源遥感影像间的域偏移问题，提升了碳储量反演精度。
原文摘要 · Abstract (English)
Forests function as crucial carbon reservoirs on land, and their carbon sinks can efficiently reduce atmospheric CO2 concentrations and mitigate climate change. Currently, the overall trend for monitoring and assessing forest carbon stocks is to integrate ground monitoring sample data with satellite remote sensing imagery. This style of analysis facilitates large-scale observation. However, these techniques require improvement in accuracy. We used GF-1 WFV and Landsat TM images to analyze Huize County, Qujing City, Yunnan Province in China. Using the style transfer method, we introduced Swin Transformer to extract global features through attention mechanisms, converting the carbon stock estimation into an image translation.
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