Reference-based super-resolution (Ref-SR) is a heated topic distinguished from single-image super-resolution (SISR). It aims at transferring more texture details from reference (Ref) image with a different perspective, to super-resolve the low-resolution (LR) image. However, the development of Ref-SR within remote sensing (RS) community is limited by three problems. First, RS images exhibit more complex texture details compared to natural images. It’s challenging to learn and reconstruct fine texture of RS images. Second, the lack of high-quality RS image dataset, which contains massive RS image pairs from different perspectives, hampers the model training and diminishes the generalization of Ref-SR within RS community. Third, the lack of physical system prevents it from verifying the feasibility of Ref-SR in RS practice. To address these problems, this paper proposes a novel reference-based gradient-assisted texture-enhancement GAN (RGTGAN), a novel dataset, namely KaggleSRD, and a novel physical simulation system, namely dual-zoom-lens system (DZLS). Specifically, this paper proposes a gradient-assisted texture-enhancement module (GTEM) to fully release the potential of gradient branch to learn fine structures during feature extraction process, a novel dense-intern deformable convolution (DIDConv) to boost the alignment effect between features from different image branches during feature alignment process, and a novel dense-restore-residual (DRR) module to effectively transfer features. Extensive experimental results on both datasets, RRSSRD and KaggleSRD, demonstrate the superiority of the proposed method over state-of-the-art methods. Furthermore, DZLS verifies promising application prospects of the proposed method. Our code and dataset are publicly available at: https://github.com/stdinR/RGTGAN.

Reference-based super-resolution (RefSR) has achieved remarkable progress and shows promising potential applications in the field of remote sensing. However, previous studies heavily rely on existing and high-resolution reference image (Ref), which is hard to obtain in remote sensing practice. To address this issue, a novel structure based on a zoom camera structure (ZCS) together with a novel RefSR network, namely AEFormer, is proposed. The proposed ZCS provides a more accessible way to obtain valid Ref than traditional fixed-length camera imaging or external datasets. The physics-enabled network, AEFormer, is proposed to super-resolve low-resolution images (LR). With reasonably aligned and enhanced attention, AEFormer alleviates the misalignment problem, which is challenging yet common in RefSR tasks. Herein, it contributes to maximizing the utilization of spatial information across the whole image and better fusion between Ref and LR. Extensive experimental results on benchmark dataset RRSSRD and real-world prototype data both verify the effectiveness of the proposed method. Hopefully, ZCS and AEFormer can enlighten a new model for future remote sensing imagery super-resolution.

As the space environment becomes more complex, efficient detection and tracking of resident space objects (RSOs) in Earth's orbit is increasingly important. However, current single-satellite detection systems are limited by observation range and computational power, making real-time RSO awareness difficult. To address this challenge, we propose a collaborative detection model with reconfigurable satellites that leverages a federated learning framework to enable situational awareness and object detection across multiple nodes. Specifically, we designed a Modular Self-Reconfigurable Satellite consisting of four modules, with each module equipped with two cameras. By stitching together their fields of view, the constellation offers comprehensive situational awareness of the surrounding 2π annular space. The approach overcomes the limitation of single-point computing power by aggregating and updating network parameters among reconfigurable satellites, effectively addressing the risks associated with multi-modules data transmission. We conduct extensive experiments to validate the effectiveness of our proposed reconfigurable satellite in detecting RSOs. Experimental results show that the optical system constructed in this paper can effectively perceive 6th magnitude stars with an exposure time of 1 second, and the proposed detection model exhibits a maximum increase in accuracy of 5.2% compared with the single satellite model.

The non-linearly curved coastal zone is very long and wide. The traditional satellite can’t cover the whole coastal zone in a single scan. So, the method of matching imaging combining the adjustment of satellite attitude and the scanning of the swing mirror are proposed. Firstly, based on the position of feature points of the coastal zone, the attitude when the optical axis of the satellite always points to the feature points is calculated. According to the width of the sea and land on both sides of the coastline, the parameters of the swing mirror of wide-swath whiskbroom payloads are analyzed. Secondly, the velocity vector model considering time-varying satellite attitude and the dynamic scanning of the swing mirror is constructed. The schemes of matching imaging such as adjustments of yaw angle and detector are developed. Finally, the precise experiment is designed to verify the correctness of the matching imaging. The experimental results show that the resolution of the matching imaging is less than 1 pixel and its modulation transfer function (MTF) is greater than the human eye’s minimum MTF of 0.026. The method of matching imaging using the adjustment of satellite attitude and the scanning of the swing mirror can realize wide imaging along the coastline and improve the temporal resolution at the same time.

Mirror scanning image mode (MSIM) effectively expands the observation range while maintaining the resolution and has become a prominent research focus in the field of Earth observation. However, it faces the inherent limitation of discontinuous imaging, which poses a major obstacle to achieving seamless multi-strip coverage. Additionally, while MSIM expands the imaging range, it also introduces complex time-varying relative motion between the image and focal plane, further increasing the difficulty of image motion compensation. Therefore, effectively addressing the limitations of discontinuous imaging and image motion has become a critical challenge in the advancement of MSIM. To address these difficulties, we introduce a novel image mode, namely deceleration-based mirror scanning imaging mode (DMSIM). This mode integrates the satellite’s pitch maneuver with MSIM, effectively expanding the overlap and aiding in image motion compensation. Specifically, DMSIM first constructs a set of parameter constraint equations to define the reasonable range for satellite and payload motion speeds, thereby ensuring seamless coverage of the target area. Subsequently, within the defined speed range, image motion compensation is achieved by designing the satellite’s pitch attitude maneuver. Moreover, scaled-down tests and digital simulations are designed to validate our mode. Experiment results indicate that this mode achieves a swath width of 600km × 600 km while the ground sample distance is at the meter level. Meanwhile, the average modulation transfer function (MTF) is 0.088, highlighting its great potential for broad coverage and sharp imaging.