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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]

The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinctive characteristics, including small size, weak energy, low contrast, and limited and varying features, rendering precise detection a challenging task. To overcome these challenges, we propose a novel detection method that extends the input of YOLOv5s to a continuous sequence of images and inter-frame optical flow, emulating the visual mechanisms employed by humans. By incorporating the image sequence as input, our model can leverage both temporal and spatial information, extracting more features of small and weak targets through the integration of spatiotemporal data. This integration augments the accuracy and robustness of drone detection. Furthermore, the inclusion of optical flow enables the model to directly perceive the motion information of drone targets across consecutive frames, enhancing its ability to extract and utilize features from dynamic objects. Comparative experiments demonstrate that our proposed method of extended input significantly enhances the network's capability to detect small moving targets, showcasing competitive performance in terms of accuracy and speed. Specifically, our method achieves a final average precision of 86.87%, representing a noteworthy 11.49% improvement over the baseline, and the speed remains above 30 frames per second. Additionally, our approach is adaptable to other detection models with different backbones, providing valuable insights for domains such as Urban Air Mobility and autonomous driving. [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 novel rotating synthetic aperture (RSA) is a new optical imaging system that uses the method of rotating the rectangular primary mirror for dynamic imaging. It has the advantage of being lightweight, with no need for splicing and real-time surface shape maintenance on orbit. The novel imaging method leads to complex image quality degradation characteristics. Therefore, it is vital to use the image quality improvement method to restore and improve the image quality to meet the application requirements. For the RSA system, a new system that has not been applied in orbit, it is difficult to construct suitable large datasets. Therefore, it is necessary to study and establish the dynamic imaging characteristic model of the RSA system, and on this basis provide data support for the corresponding image super resolution and restoration method through simulation. In this paper, we first analyze the imaging characteristics and mathematically model the rectangular rotary pupil of the RSA system. On this basis, combined with the analysis of the physical interpretation of the blur kernel, we find that the optimal blur kernel is not the point spread function (PSF) of the imaging system. Therefore, the simulation method of convolving the input image directly with the PSF is flawed. Furthermore, the weights of a convolutional neural network (CNN) are the same for each input. This means that the normal convolutional layer is not only difficult to accurately estimate the time-varying blur kernel, but also difficult to adapt to the change in the length–width ratio of the primary mirror. To that end, we propose a blur kernel estimation conditional convolutional neural network (CCNN) that is equivalent to multiple normal CNNs. We extend the CNN to a conditional model by taking an encoding as an additional input and using conditionally parameterized convolutions instead of normal convolutions. The CCNN can simulate the imaging characteristics of the rectangular pupil with different length–width ratios and different rotation angles in a controllable manner. The results of semi-physical experiments show that the proposed simulation method achieves a satisfactory performance, which can provide data and theoretical support for the image restoration and super-resolution method of the RSA system. [ABSTRACT FROM AUTHOR]

Diffractive membrane imaging systems have been an important development trend for high-orbit satellite cameras owing to their advantages of large aperture, light weight, rapid manufacture, and low cost. However, caused by the cross-coupling effects of diffraction imaging, membrane properties, subaperture stitching, on-orbit disturbances, and other physical factors, lager-aperture space diffractive membrane imaging systems have specific and complex degradation characteristics: the modulation transfer function (MTF) and signal-to-noise ratio (SNR) have more prominent degradation and serious space-variant characteristics over fields of view, with obvious background radiation properties that seriously affect the application of imaging products. To address this problem, this study established a global information transmission model by characterizing the PSF and background radiation in a full field of view to represent the imaging law of an on-orbit system. Aiming at the inverse problem of the information transmission model, we also propose a novel image inversion restoration method for the special degradation characteristics. In particular, the effect of diffraction efficiency is introduced into the inversion restoration method to solve the background radiation problem. Moreover, we innovatively designed matrix regularization parameters to further improve the correction ability of spatial variation. When the diffraction efficiency was experimentally higher than 60% and the mean measured spatial variability was less than 0.2, the proposed method exhibited a satisfactory processing performance, and could improve multiobjective comprehensive processing, such as transfer function compensation, spatial variation correction, and background radiation removal. [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]

• The imaging mechanism of the novel rotating synthetic aperture system is deduced. • The image degradation model of the rotating synthetic aperture system is presented. • The effects of factors causing image quality degradation are analyzed. The Rotating synthetic aperture (RSA) is one of the most promising novel imaging systems to meet the requirements of lightweight and high resolution for optical remote sensing satellites, especially in geostationary orbit. The point spread function (PSF) orientation relative to the observed scene, intentionally changes to measure all spatial frequency information available to a filled circular aperture having a diameter approximately equal to the length of the rectangular aperture. As with all precision optical systems, RSA is also subject to dynamic disturbances from the spacecraft/environment. If not accommodated properly in the measurement and synthesis process, the dynamic disturbances can result in significant image quality degradation. To quantify methods to reduce or eliminate the errors, we first analyze the on-orbit imaging mechanism of the RSA system theoretically and then represent the worst-case on-orbit imaging degradation characteristics. [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]

In this paper, we present a model that analyzes the influence of the temperature on the modulation transfer function (MTF) of an infrared membrane diffraction optical system. Based on the physical imaging mechanism of diffractive optical systems, the imaging characteristics of the diffraction optical system are characterized by using diffraction efficiency. Then we establish the corresponding calculation model and MTF analysis model based on scalar diffraction theory. In particular, considering the material properties of flexible membrane optical elements and thermal environment effect, the Zernike polynomials are used to establish the thickness distribution model of the membrane diffractive optical components combined with the finite element analysis. After that, we modify the MTF calculation model of diffraction optical system and accordingly propose the model of the infrared membrane diffraction optical system. Finally, we present some experimental results for a 2-m infrared membrane diffraction optical system. The results show that when the temperature is -40°C and wavelength band is 80 nm at the center field of view, the diffraction efficiency is reduced to 81%, and MTF at 75 l p/mm is reduced to 0.12. And when the temperature is -40°C at edge field of view, the efficiency at center wavelength is reduced to 71%, and MTF at 75 l p/mm is reduced to 0.02. The established model and method can be used to estimate the imaging performance of a space infrared membrane diffractive telescope and provide theoretical guidance for the processing algorithm and imaging system design.