Rotating Synthetic Aperture (RSA) technology is one of the distinctly advantageous Earth geostationary orbit optical remote sensing technologies. However, the continuous rotation of the RSA system’s rectangular primary mirror results in a discernible drop in resolution along the shorter side of the mirror. Additionally, the captured images exhibit periodic and time-varying characteristics. To improve the image quality to meet interpretation needs, we first delineate the imaging process of the rotating primary mirror and analyze the characteristics of image degradation based on the system’s imaging mechanism. Then, we propose a dual super-resolution (SR) framework based on Swin Transformer and introduce a self-supervised learning method for jointly training the unified SR network using wavelet fusion. The self-supervised learning method effectively utilizes the spatiotemporal correlation of the information contained in images captured at different rotation directions of the rectangular pupil. Moreover, the attention mechanism in Transformer can adopt a global perspective and utilize content-based interactions between image content and attention weights to model strong long-range dependencies in remote sensing images. This approach significantly enhances image quality along the pupil’s shorter side, consequently yielding superior results. Extensive digital and semi-physical imaging experiments, involving six aspect ratios of the primary mirror, demonstrate that our SR method surpasses state-of-the-art methods. The work in this paper can serve as a valuable reference for future space applications of the RSA technology.

中文翻译：

---

基于Transformer的多时相融合旋转合成孔径系统自监督图像超分辨率方法

旋转合成孔径(RSA)技术是地球静止轨道光学遥感技术中具有明显优势的技术之一。然而，RSA 系统的矩形主镜的连续旋转会导致沿镜面较短侧的分辨率明显下降。此外，捕获的图像表现出周期性和时变的特征。为了提高图像质量以满足判读需求，我们首先描绘了旋转主镜的成像过程，并根据系统的成像机理分析了图像劣化的特征。然后，我们提出了一种基于 Swin Transformer 的双超分辨率（SR）框架，并引入了一种使用小波融合联合训练统一 SR 网络的自监督学习方法。自监督学习方法有效地利用了矩形瞳孔不同旋转方向捕获的图像中包含的信息的时空相关性。此外，Transformer 中的注意力机制可以采用全局视角，利用图像内容和注意力权重之间基于内容的交互来建模遥感图像中的强远程依赖性。这种方法显着提高了瞳孔短边的图像质量，从而产生了优异的结果。广泛的数字和半物理成像实验，涉及主镜的六个长宽比，证明我们的 SR 方法超越了最先进的方法。本文的工作可为RSA技术未来的空间应用提供有价值的参考。