用改进的生成模型提升遥感图像碳储量估算精度
Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery
- 引入知识蒸馏与跨注意力融合，优化特征提取
- 模型RMSE达12.17%，较回归模型提升41.69%~42.33%
- 适合遥感碳汇监测与区域生态管理应用
森林是陆地最主要的碳库，有效降低大气CO2浓度，缓解气候变化。遥感提供高精度数据，支持大范围长期监测。本研究以云南曲靖市富源县为研究区，利用GF-1 WFV卫星影像，提出改进的隐式扩散模型（IIDM），引入KD-VGG和KD-UNet模块进行初始特征提取。结果表明：(1) VGG模块优化参数后提升了特征提取精度并缩短推理时间；(2) Cross-attention + MLPs模块实现全局与局部特征的有效融合，建立关键关联；(3) IIDM模型在碳储量估计中表现最优，均方根误差（RMSE）为12.17%，相比传统回归模型提升41.69%至42.33%。生成模型在深层特征提取上优势显著，验证了AI生成内容在定量遥感中的可行性。16米分辨率的估算结果为制定森林碳汇政策、加强区域碳储量管理提供了可靠依据。
原文摘要 · Abstract (English)
The forest serves as the most significant terrestrial carbon stock mechanism, effectively reducing atmospheric CO2 concentrations and mitigating climate change. Remote sensing provides high data accuracy and enables large-scale observations. Optical images facilitate long-term monitoring, which is crucial for future carbon stock estimation studies. This study focuses on Huize County, Qujing City, Yunnan Province, China, utilizing GF-1 WFV satellite imagery. The KD-VGG and KD-UNet modules were introduced for initial feature extraction, and the improved implicit diffusion model (IIDM) was proposed. The results showed: (1) The VGG module improved initial feature extraction, improving accuracy, and reducing inference time with optimized model parameters. (2) The Cross-attention + MLPs module enabled effective feature fusion, establishing critical relationships between global and local features, achieving high-accuracy estimation. (3) The IIDM model, a novel contribution, demonstrated the highest estimation accuracy with an RMSE of 12.17%, significantly improving by 41.69% to 42.33% compared to the regression model. In carbon stock estimation, the generative model excelled in extracting deeper features, significantly outperforming other models, demonstrating the feasibility of AI-generated content in quantitative remote sensing. The 16-meter resolution estimates provide a robust basis for tailoring forest carbon sink regulations, enhancing regional carbon stock management.
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