用深度学习从SAR图像直接估树高，提升全球森林监测效率
Tomographic SAR Reconstruction for Forest Height Estimation
- 跳过传统三维重建流程，直接从2D SAR图像预测树高
- 使用少量SAR图像时误差比完整处理高16-21%
- 适合需要快速响应的卫星数据采集与森林动态监测
树高估测是生态与林业中估算生物量的重要指标。传统方法如摄影测量和激光雷达（LiDAR）虽精度高，但全球应用成本高昂且实施困难。相比之下，合成孔径雷达（SAR）遥感技术具备全天候观测能力，适用于大范围树高估计。本文利用深度学习，直接从二维单视复数（SLC）SAR图像估计森林冠层高度，绕过传统的三维断层成像信号处理流程，有望降低从数据获取到结果产出的时间延迟。同时，研究量化了不同数量SLC图像对高度估计精度的影响，为未来卫星任务的数据采集策略优化提供依据。与完整断层成像处理结合深度学习的方法相比，本方法仅采用部分处理加深度学习，误差高出16%-21%，凸显几何信号处理在精度上的持续重要性。
原文摘要 · Abstract (English)
Tree height estimation serves as an important proxy for biomass estimation in ecological and forestry applications. While traditional methods such as photogrammetry and Light Detection and Ranging (LiDAR) offer accurate height measurements, their application on a global scale is often cost-prohibitive and logistically challenging. In contrast, remote sensing techniques, particularly 3D tomographic reconstruction from Synthetic Aperture Radar (SAR) imagery, provide a scalable solution for global height estimation. SAR images have been used in earth observation contexts due to their ability to work in all weathers, unobscured by clouds. In this study, we use deep learning to estimate forest canopy height directly from 2D Single Look Complex (SLC) images, a derivative of SAR. Our method attempts to bypass traditional tomographic signal processing, potentially reducing latency from SAR capture to end product. We also quantify the impact of varying numbers of SLC images on height estimation accuracy, aiming to inform future satellite operations and optimize data collection strategies. Compared to full tomographic processing combined with deep learning, our minimal method (partial processing + deep learning) falls short, with an error 16-21\% higher, highlighting the continuing relevance of geometric signal processing.
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