用复数神经网络重建极化SAR图像，保留关键物理特性。
Exploring Polarimetric Properties Preservation during Reconstruction of PolSAR images using Complex-valued Convolutional Neural Networks
- 直接处理复数域数据的卷积自编码器
- 多种分解方法验证物理特性保留效果
- 适合需要物理一致性分析的遥感研究者
极化合成孔径雷达（PolSAR）数据具有固有的复数特性，需采用能直接处理复数表示的专用算法。然而，深度学习领域对此关注不足，许多研究将复数信号转换为实数域后使用传统实值模型。本文采用复数域神经网络，研究复数卷积自编码器在全极化SAR数据压缩与重构中的表现。结果表明，该方法能有效压缩并重建数据，同时通过Pauli、Krogager、Cameron相干分解及非相干H-α分解验证了关键物理特性的保留。最后，凸显复数神经网络相比实数模型的优势。这些发现为构建鲁棒、物理信息驱动的复数生成模型奠定了基础。
原文摘要 · Abstract (English)
The inherently complex-valued nature of Polarimetric SAR data necessitates using specialized algorithms capable of directly processing complex-valued representations. However, this aspect remains underexplored in the deep learning community, with many studies opting to convert complex signals into the real domain before applying conventional real-valued models. In this work, we leverage complex-valued neural networks and investigate the performance of complex-valued Convolutional AutoEncoders. We show that these networks can effectively compress and reconstruct fully polarimetric SAR data while preserving essential physical characteristics, as demonstrated through Pauli, Krogager, and Cameron coherent decompositions, as well as the non-coherent $H-α$ decomposition. Finally, we highlight the advantages of complex-valued neural networks over their real-valued counterparts. These insights pave the way for developing robust, physics-informed, complex-valued generative models for SAR data processing.
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