The object detection of unmanned aerial vehicle (UAV) images has widespread applications in numerous fields; however, the complex background, diverse scales, and uneven distribution of objects in UAV images make object detection a challenging task. This study proposes a convolution neural network (CNN)-transformer hybrid model to achieve efficient object detection in UAV images, which has three advantages that contribute to improving the object detection performance. First, the efficient and effective cross-shaped window (CSWin) transformer can be used as a backbone to obtain image features at different levels, and the obtained features can be input into the feature pyramid network (FPN) to achieve multi-scale representation, which will contribute to multi-scale object detection. Second, a hybrid patch embedding module is constructed to extract and utilize low-level information such as the edges and corners of the image. Finally, a slicing-based inference method is constructed to fuse the inference results of the original image and sliced images, which will improve the small object detection accuracy without modifying the original network. Experimental results on public datasets illustrate that the proposed method can improve the performance more effectively than several popular and state-of-the-art object detection methods.

The SuperGlue algorithm, which integrates deep learning theory with the SuperPoint feature extraction operator and addresses the matching problem using the classical Sinkhorn method, has significantly enhanced matching efficiency and become a prominent research focus. However, existing feature extraction operators often struggle to extract high-quality features from extremely low-light or dark images, resulting in reduced matching accuracy. In this study, we propose a novel feature matching method that combines multi-scale retinex with color restoration (MSRCR) and SuperGlue to address this challenge, enabling effective feature extraction and matching from dark images, successfully addressing the challenges of feature point extraction difficulties, sparse matching points, and low matching accuracy in extreme environments such as nighttime autonomous navigation, mine exploration, and tunnel operations. Our approach first employs the retinex-based MSRCR algorithm to enhance features in original low-light images, followed by utilizing the enhanced image pairs as inputs for SuperGlue feature matching. Experimental results validate the effectiveness of our method, demonstrating that both the quantity of extracted feature points and correctly matched feature points approximately doubles compared to traditional methods, thereby significantly improving matching accuracy in dark images.

To effectively guarantee the safety and efficiency of space activities, and to meet the needs of different levels of users for space object situation (SOS) knowledge, formalized and standardized SOS knowledge is needed to assist cognition. A knowledge graph (KG), as a suitable technology that can formally express knowledge and construct a standardized knowledge base, can provide knowledge generation, representation and intelligent service of SOS. In this paper, an SOS information service based on the KG (SOSKG) was constructed. Aimed at different scales of components in SOS, a multi-granularity knowledge structure was constructed, and a formalized expression of SOSKG was realized. To incorporate the complex relations into the SOSKG, a multi-level semantic relation parsing model and formalized representation were proposed from the three aspects of basic relations, spatial relations, and temporal relations. Additionally, a multi-element knowledge construction model was constructed to address the dynamic characteristics of various relations in SOSKG under a complex spatio-temporal environment. This study constructed different cases and scenarios to test the service, and the results show that the proposed service can effectively organize and express SOS knowledge in a complex spatio-temporal environment, provide an intermediate bridge between users and SOS knowledge, and promote users' cognition of SOS knowledge.

A digital twin city maps a virtual three-dimensional (3D) city model to the geographic information system, constructs a virtual world, and integrates real sensor data to achieve the purpose of virtual–real fusion. Focusing on the accuracy problem of vision sensor registration in the virtual digital twin city scene, this study proposes a 2D-3D registration method under inverse depth and structural semantic constraints. First, perspective and inverse depth images of the virtual scene were obtained by using perspective view and inverse-depth nascent technology, and then the structural semantic features were extracted by the two-line minimal solution set method. A simultaneous matching and pose estimation method under inverse depth and structural semantic constraints was proposed to achieve the 2D-3D registration of real images and virtual scenes. The experimental results show that the proposed method can effectively optimize the initial vision sensor pose and achieve high-precision registration in the digital twin scene, and the Z-coordinate error is reduced by 45%. An application experiment of monocular image multi-object spatial positioning was designed, which proved the practicability of this method, and the influence of model data error on registration accuracy was analyzed.

Due to significant nonlinear radiometric differences (NRD) and severe geometric distortions (e.g., translation, scale, and rotation) among multimodal images (e.g., optical, infrared, SAR), image registration remains a challenging problem. This paper proposes a robust matching method based on self-similarity adjacent self-convolution features (SASF), consisting of two key steps. In the first stage, a uniform distribution of feature points in multimodal images is extracted using a FAST feature extraction operator based on normalized filtering, and geometric correction is performed using prior reference geographic location information of remote sensing images to achieve coarse registration. In the second stage, addressing the significant NRD, a novel structural feature SASF is constructed. Based on this, a rapid structural similarity measurement method ($Fast-SSIM_{SASF}$) is proposed, which utilizes 3D fast Fourier transform techniques to enhance image matching efficiency. The proposed SASF algorithm is evaluated for performance on multiple sets of multimodal remote sensing images. Experimental results demonstrate that this method outperforms many existing excellent algorithms in terms of matching effectiveness. Finally, the SASF algorithm is tested on four pairs of entire-scene multimodal remote sensing satellite images, demonstrating its robustness to nonlinear radiometric differences and geometric distortions.

The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling; in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience.