标题： 基于深度学习的北西班牙L波段干涉SAR时间序列高分辨率森林制图 显示英文标题

标题： High-Resolution Forest Mapping from L-Band Interferometric SAR Time Series using Deep Learning over Northern Spain

摘要： 在本研究中，我们考察了使用L波段干涉时序数据集和深度学习建模在高分辨率森林制图中的潜力。 我们的SAR数据由九张ALOS-2 PALSAR-2双极化SAR图像组成，这些图像在西班牙北部阿斯图里亚斯研究区域的近零空间基线上获取。 参考数据通过机载激光扫描收集。 我们评估了UNet家族中几种候选深度学习模型的性能，这些模型结合了不同的输入极化和干涉特征。 除了基本的Vanilla UNet外，还使用了带有挤压-激励块（SeU-Net）的注意力增强UNet模型以及具有嵌套结构和跳跃路径的高级UNet模型。 研究的特征包括双极化干涉观测值，同时还加入了基于模型推导出的度量。 结果表明，与仅使用后向散射强度相比，添加基于模型的反演InSAR特征或InSAR相干层可以提高检索精度。 使用注意力机制和嵌套连接融合提供了比使用Vanilla UNet或传统机器学习方法更好的预测结果。 当仅使用强度数据时，在20米分辨率下森林高度的检索精度范围为3.1-3.8米（R2 = 0.45--0.55），当同时包含强度和干涉相干特征时，精度提高到低于2.8米。 在40米和60米分辨率下，检索性能进一步提高，这主要是由于强度和干涉层中的信噪比更高。 当在60米分辨率下使用强度时，最佳RMSE为2.2米，而当使用所有合适的输入特征时，达到的误差为1.95米。我们建议这种混合方法也适用于L波段SAR检索，并且适合NISAR和未来的ROSE-L任务。 显示英文摘要

摘要： In this study, we examine the potential of high-resolution forest mapping using L-band interferometric time series datasets and deep learning modeling. Our SAR data are represented by a time series of nine ALOS-2 PALSAR-2 dual-pol SAR images acquired at near-zero spatial baseline over a study site in Asturias, Northern Spain. Reference data are collected using airborne laser scanning. We examine the performance of several candidate deep learning models from UNet-family with various combinations of input polarimetric and interferometric features. In addition to basic Vanilla UNet, attention reinforced UNet model with squeeze-excitation blocks (SeU-Net) and advanced UNet model with nested structure and skip pathways are used. Studied features include dual pol interferometric observables additionally incorporating model-based derived measures. Results show that adding model-based inverted InSAR features or InSAR coherence layers improves retrieval accuracy compared to using backscatter intensity only. Use of attention mechanisms and nested connection fusion provides better predictions than using Vanilla UNet or traditional machine learning methods. Forest height retrieval accuracies range between 3.1-3.8 m (R2 = 0.45--0.55) at 20 m resolution when only intensity data are used, and improve to less than 2.8 m when both intensity and interferometric coherence features are included. At 40 m and 60 m resolution, retrieval performance further improves, primarily due to higher SNR in both the intensity and interferometric layers. When using intensity at 60 m resolution, best achieved RMSE is 2.2 m, while when using all suitable input features the achieved error is 1.95 m. We recommend this hybrid approach for L-band SAR retrievals also suitable for NISAR and future ROSE-L missions.