基于方位向多普勒分解的哨兵一号条带图增强深度学习迭代框架
A Deep Learning Iterative Framework for Sentinel-1 Stripmap Enhancement Based on Azimuth Doppler Decomposition
AI总结：
提出一种基于方位子孔径分解的自监督增强框架，利用子孔径与全孔径图像之间的物理一致性生成训练数据，通过单/多帧学习和迭代推理逐步提升图像质量，在哨兵一号条带模式数据上优于MERLIN方法。
摘要：
合成孔径雷达（SAR）图像能够实现全天候、昼夜地球观测；然而，由于散斑噪声和其他固有成像伪影，其仍难以解释。哨兵一号（S1）是最广泛使用的星载SAR任务之一，提供系统性的全球覆盖、高时间分辨率、双极化成像和免费数据获取。在S1模式中，条带图（SM）提供最高分辨率，但散斑噪声和空间约束常常阻碍需要更精细空间细节的应用。这激发了对有效图像增强策略的需求。在这项工作中，我们提出了一种基于方位子孔径分解的S1 SM图像自监督增强框架。该方法利用子孔径重建与对应全孔径图像之间的物理一致性，生成配对训练数据，无需外部传感器、模拟真值或多时相堆叠。所提框架集成了单帧和多帧学习，并融入迭代推理方案，逐步提升图像质量。在真实S1 SM数据上的实验表明，所提方法在PSNR和SSIM上持续优于广泛采用的自监督深度学习基线MERLIN，而MERLIN获得更高的ENL，凸显了结构保真度与散斑平滑之间的权衡。总体而言，结果表明基于子孔径的监督为使用S1数据的SAR图像增强提供了一种物理基础、可复现且操作可行的方法。值得注意的是，所提方法可扩展到其他SAR平台、极化和采集模式。
英文摘要：
Synthetic Aperture Radar (SAR) imagery enables all-weather, day-and-night Earth observation; however, it remains difficult to interpret due to speckle noise and other intrinsic imaging artifacts. Sentinel-1 (S1) constitutes one of the most widely used spaceborne SAR missions, offering systematic global coverage, high temporal resolution, dual-polarization imaging, and free data availability. Among S1 modes, Stripmap (SM) provides the highest resolution, yet speckle noise and spatial constraints often hinder applications requiring finer spatial detail. This motivates the need for effective image enhancement strategies. In this work, we propose a self-supervised enhancement framework for S1 SM imagery based on azimuth subaperture decomposition. The method exploits the physical consistency between subaperture reconstructions and the corresponding full-aperture image to generate paired training data without external sensors, simulated ground truth, or multi-temporal stacks. The proposed framework integrates single- and multi-frame learning and incorporates an iterative inference scheme that progressively refines image quality. Experiments on real S1 SM data show that the proposed approach consistently outperforms the widely adopted self-supervised deep learning baseline MERLIN, in terms of PSNR and SSIM, while MERLIN attains higher ENL, highlighting a trade-off between structural fidelity and speckle smoothing. Overall, the results demonstrate that subaperture-based supervision provides a physically grounded, reproducible, and operationally viable approach for SAR image enhancement using S1 data. It is worth noting that the proposed approach can be extended to other SAR platforms, polarizations, and acquisition modes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
