标题： 基于物理启发表示学习的复值SAR基础模型 显示英文标题

标题： A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning

摘要： 视觉基础模型在遥感领域已被广泛研究，因为它们在各种下游任务中表现出优越的泛化能力。 合成孔径雷达（SAR）提供了全天候成像能力，为地球观测提供了显著优势。 然而，建立用于SAR图像解释的基础模型不可避免地会遇到信息利用不足和可解释性差的挑战。 在本文中，我们提出了一种基于复数SAR数据的遥感基础模型，该模型模拟极化分解过程进行预训练，即通过散射基和散射系数的加权组合来表征像素散射强度，从而赋予基础模型物理可解释性。 具体而言，我们构建了一系列散射查询，每个查询代表一个独立且有意义的散射基，这些查询与散射查询解码器中的SAR特征交互，并输出相应的散射系数。 为了引导预训练过程，构建了极化分解损失和功率自监督损失。前者将预测的系数与Yamaguchi系数对齐，后者则从预测的系数中重建功率并将其与输入图像的功率进行比较。 我们的基础模型在六个典型的下游任务上进行了验证，取得了最先进的结果。 值得注意的是，该基础模型可以提取稳定的特征表示，并在数据稀缺的情况下表现出强大的泛化能力。 显示英文摘要

摘要： Vision foundation models in remote sensing have been extensively studied due to their superior generalization on various downstream tasks. Synthetic Aperture Radar (SAR) offers all-day, all-weather imaging capabilities, providing significant advantages for Earth observation. However, establishing a foundation model for SAR image interpretation inevitably encounters the challenges of insufficient information utilization and poor interpretability. In this paper, we propose a remote sensing foundation model based on complex-valued SAR data, which simulates the polarimetric decomposition process for pre-training, i.e., characterizing pixel scattering intensity as a weighted combination of scattering bases and scattering coefficients, thereby endowing the foundation model with physical interpretability. Specifically, we construct a series of scattering queries, each representing an independent and meaningful scattering basis, which interact with SAR features in the scattering query decoder and output the corresponding scattering coefficient. To guide the pre-training process, polarimetric decomposition loss and power self-supervision loss are constructed. The former aligns the predicted coefficients with Yamaguchi coefficients, while the latter reconstructs power from the predicted coefficients and compares it to the input image's power. The performance of our foundation model is validated on six typical downstream tasks, achieving state-of-the-art results. Notably, the foundation model can extract stable feature representations and exhibits strong generalization, even in data-scarce conditions.