用于利用光学和极化干涉合成孔径雷达数据进行热带森林生物量估计的保相三模态变压器
Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data
AI总结：
针对成熟热带森林地上生物量估计难题，提出保相三模态变压器，融合光学与极化干涉合成孔径雷达数据，用复值编码器和动态协同注意力机制，还推导校准模型，效果优于基线，为热带生物群落碳储量测绘提供框架。
摘要：
成熟热带森林地上生物量（AGB）的准确估计在遥感中仍是关键挑战。为克服物理限制，我们提出三模态相干协同注意力变压器（TCCT），一种物理知识引导的深度学习架构。TCCT原生融合光学表面反射率（Landsat - 5）与P和L波段的复值极化干涉合成孔径雷达（PolInSAR）数据。……通过满足欧洲航天局（ESA）生物量任务误差小于20%的要求，TCCT为热带生物群落的连续碳储量测绘提供了一个强大的框架。
英文摘要：
The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the saturation of Synthetic Aperture Radar (SAR) signals in high-density areas and persistent cloud cover affecting optical imagery. To overcome these physical limitations, we propose the Trimodal Coherent Co-attention Transformer (TCCT), a physics-informed deep learning architecture. The TCCT natively fuses optical surface reflectance (Landsat-5) with complex-valued Polarimetric SAR Interferometry (PolInSAR) data from both P and L bands. Unlike traditional fusion methods, our architecture employs complex-valued encoders to preserve spatial phase coherence, coupled with a dynamic co-attention mechanism that acts as an adaptive gating module, reducing the weight of cloud-corrupted optical pixels and shifting reliance to microwave phase data. We also derived a localized spatial allometric calibration model via Levenberg-Marquardt optimization, tailored to the specific wood density of the Paracou region in the Amazon basin. Evaluated using a two-stage protocol, the TCCT first underwent a rigorous 5-fold cross-validation to establish robust global weights (achieving a global RMSE of 4.19 m). Subsequently, following a localized spatial fine-tuning phase over 200 epochs, the model attained an absolute RMSE of 3.78 m and an $R^2$ of 0.33 for Canopy Height Models (CHM), outperforming standard Random Forest, CNN, and Vision Transformer baselines. Our ablation study confirms that preserving phase coherence mitigates deep-canopy signal saturation. When converted to AGB, the fine-tuned TCCT map yielded a Relative RMSE (rRMSE) of 4.51% in dense forest areas above 50 Mg/ha. By meeting the European Space Agency (ESA) BIOMASS mission requirement of less than 20% error, the TCCT provides a robust framework for continuous carbon stock mapping in tropical biomes.
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