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The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system. [ABSTRACT FROM AUTHOR]

With the increasing demand for the wide-area refined detection of aircraft targets, remote sensing cameras have adopted an ultra-large area-array detector as a new imaging mode to obtain broad width remote sensing images (RSIs) with higher resolution. However, this imaging technology introduces new special image degradation characteristics, especially the weak target energy and the low signal-to-noise ratio (SNR) of the image, which seriously affect the target detection capability. To address the aforementioned issues, we propose an aircraft detection method for RSIs with low SNR, termed L-SNR-YOLO. In particular, the backbone is built blending a swin-transformer and convolutional neural network (CNN), which obtains multiscale global and local RSI information to enhance the algorithm's robustness. Moreover, we design an effective feature enhancement (EFE) block integrating the concept of nonlocal means filtering to make the aircraft features significant. In addition, we utilize a novel loss function to optimize the detection accuracy. The experimental results demonstrate that our L-SNR-YOLO achieves better detection performance in RSIs than several existing advanced methods. [ABSTRACT FROM AUTHOR]

• The influence of coupling factors on image quality is analyzed. • The regularized multi-frame image restoration method is put forward. • The effect of rotation speed on the restoration performance is discussed. Large field circular scanning system obtains remote sensing images by rotating scanning, which is one of the technical ways to effectively expand the field of view of geostationary orbit optical remote sensing imaging system. However, due to its special rotation scanning imaging mechanism, the on-orbit imaging process of this system is affected by the coupling of detector rotation sampling, satellite platform vibration, optical system aberration and other factors, so the image degradation characteristics are very complex. In this paper, the on-orbit imaging characteristics of large field circular scanning system are first analyzed to provide a theoretical basis for the subsequent research of image restoration method. Secondly, aiming at the problems of annular geometric distortion and spatial blur of the system image, the image restoration method is proposed based on ring block and regularization methodology. Experimental results show that under the condition of detector rotation speed ω <0.06 rad/s, the proposed method exhibited a satisfactory processing performance. [ABSTRACT FROM AUTHOR]

The vehicle detection in remote sensing images is a challenging task due to the small size of the objects and interference of a complex background. Traditional methods require a large number of anchor boxes, and the intersection rate between these anchor boxes and an object's real position boxes needs to be high enough. Moreover, the size and aspect ratio of each anchor box need to be designed manually. For small objects, more anchor boxes need to be set. To solve these problems, we regard the small object as a keypoint in the relevant background and propose an anchor-free vehicle detection network (AVD-kpNet) to robustly detect small-sized vehicles in remote sensing images. The AVD-kpNet framework fuses features across layers with a deep layer aggregation architecture, preserving the fine features of small objects. First, considering the correlation between the object and the surrounding background, a 2D Gaussian distribution strategy is adopted to describe the ground truth, instead of a hard label approach. Moreover, we redesign the corresponding focus loss function. Experimental results demonstrate that our method has a higher accuracy for the small-sized vehicle detection task in remote sensing images compared with several advanced methods. [ABSTRACT FROM AUTHOR]

• A prior analysis method of remote sensing image characteristics is presented based on hyper-Laplacian regression. • A novel regularization model is established by taking the characteristic prior parameters and image together as the optimization target. • The solving approach is presented based on the alternate iteration of damping least square and iterative jumping thresholding algorithm. • A fine-processing method based on the adaptive hyper-Laplacian prior is put forward, combined with the idea of block processing. High-quality remote sensing images have wide application prospects in agroforestry investigation, target monitoring, disaster prevention, urban planning and military defense. However, remote sensing imaging links such as atmosphere, platform and optical system seriously affect the ability of image interpretation and analysis. The traditional regularized processing methods have a strong ability to improve the definition, but most of them may sacrifice texture details or introduce artifacts, because their fixed prior parameters cannot fully adapt to various kinds of scenes. To address this problem, we propose a novel fine-processing method based on the adaptive hyper-Laplacian prior for remote sensing imaging systems. The method is developed by automatically updating and optimizing the prior parameters and objective function in the iterative process based on the prior characteristics of different regions of remote sensing images. Experimentally, the proposed method can realize the fine-processing of remote sensing images, including the edge enhancement, texture detail preservation, and artifact suppression. [ABSTRACT FROM AUTHOR]