High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.

With the rapid development of remote sensing technology, optical remote sensing images are increasingly being used in areas such as military reconnaissance, environmental monitoring, and urban planning. Due to the small number of pixels, fuzzy features, and complex background, it is difficult for conventional convolutions to effectively extract features from small objects. To address this problem, we propose to use multi-scale dilated convolutions to increase the receptive field size of the model to adapt to changes in object size, capture multi-scale contextual information of the feature map, and extract richer object features. First, we propose a Dilated Convolutional Residual (DCR) module for high-level feature extraction in the network. Second, the context aggregation (CONTEXT) module uses remote interaction to perform associative computation on images using contextual aggregation, allowing the model to understand the global semantic information of the image. We propose a novel object detection method, DCN-YOLO, which achieves an AP50 of 56.6 on the AI-TOD dataset, effectively improving the detection accuracy and robustness of small objects in remote sensing images. It provides a new technical approach to the detection of small objects in remote sensing.

Space object recognition is the basis of space attack and defense confrontation. High-quality space object images are very important for space object recognition. Because of the large number of cosmic rays in the space environment and the inadequacy of optical lenses and detectors on satellites to support high-resolution imaging, most of the images obtained are blurred and contain a lot of cosmic-ray noise. So, denoising methods and super-resolution methods are two effective ways to reconstruct high-quality space object images. However, most super-resolution methods could only reconstruct the lost details of low spatial resolution images, but could not remove noise. On the other hand, most denoising methods especially cosmic-ray denoising methods could not reconstruct high-resolution details. So in this paper, a deep convolutional neural network (CNN)-based single space object image denoising and super-resolution reconstruction method is presented. The noise is removed and the lost details of the low spatial resolution image are well reconstructed based on one very deep CNN-based network, which combines global residual learning and local residual learning. Based on a dataset of satellite images, experimental results demonstrate the feasibility of our proposed method in enhancing the spatial resolution and removing the noise of the space objects images.

In this paper, a tracking and pointing control system with dual-FSM (fast steering mirror) composite axis is proposed. It is applied to the target-tracking accuracy control in a 3D GISC LiDAR (three-dimensional ghost imaging LiDAR via sparsity constraint) system. The tracking and pointing imaging control system of the dual-FSM 3D GISC LiDAR proposed in this paper is a staring imaging method with multiple measurements, which mainly solves the problem of high-resolution remote-sensing imaging of high-speed moving targets when the technology is transformed into practical applications. In the research of this control system, firstly, we propose a method that combines motion decoupling and sensor decoupling to solve the mechanical coupling problem caused by the noncoaxial sensor installation of the FSM. Secondly, we suppress the inherent mechanical resonance of the FSM in the control system. Thirdly, we propose the optical path design of a dual-FSM 3D GISC LiDAR tracking imaging system to solve the problem of receiving aperture constraint. Finally, after sufficient experimental verification, our method is shown to successfully reduce the coupling from 7% to 0.6%, and the precision tracking bandwidth reaches 300 Hz. Moreover, when the distance between the GISC system and the target is 2.74 km and the target flight speed is 7 m/s, the tracking accuracy of the system is improved from 15.7 μrad (σ) to 2.2 μrad (σ), and at the same time, the system recognizes the target contour clearly. Our research is valuable to put the GISC technology into practical applications.

To address challenges in small object detection within drone aerial imagery—such as sparse feature information, intense background interference, and drastic scale variations—this paper proposes YOLO-SR, a lightweight detection algorithm based on attention enhancement and feature reuse mechanisms. First, we designed the lightweight feature extraction module C2f-SA, which incorporates Shuffle Attention. By integrating channel shuffling and grouped spatial attention mechanisms, this module dynamically enhances edge and texture feature responses for small objects, effectively improving the discriminative power of shallow-level features. Second, the Spatial Pyramid Pooling Attention (SPPC) module captures multi-scale contextual information through spatial pyramid pooling. Combined with dual-path (channel and spatial) attention mechanisms, it optimizes feature representation while significantly suppressing complex background interference. Finally, the detection head employs a decoupled architecture separating classification and regression tasks, supplemented by a dynamic loss weighting strategy to mitigate small object localization inaccuracies. Experimental results on the RGBT-Tiny dataset demonstrate that compared to the baseline model YOLOv5s, our algorithm achieves a 5.3% improvement in precision, a 13.1% increase in recall, and respective gains of 11.5% and 22.3% in mAP_0.5 and mAP_0.75, simultaneously reducing the number of parameters by 42.9% (from 7.0 × 10⁶ to 4.0 × 10⁶) and computational cost by 37.2% (from 60.0 GFLOPs to 37.7 GFLOPs). The comprehensive improvement across multiple metrics validates the superiority of the proposed algorithm in both accuracy and efficiency.