Cross-view geolocalization of unmanned aerial vehicles (UAVs) is a challenging task due to the positional discrepancies and uncertainties in scale and distance between UAVs and satellite views. Existing transformer-based geolocalization methods mainly use encoders to mine image contextual information. However, these methods have some limitations when dealing with scale changes in cross-view images. Therefore, we present an effective transformer-based Siamese network tailored for UAV geolocalization, called GeoFormer. First, an efficient transformer feature extraction network was designed, which utilizes linear attention to reduce the computational complexity and improve the computational efficiency of the network. Among them, we designed an efficient separable perceptron module based on depthwise separable convolution, which can effectively reduce the computational cost while improving the feature representation of the network. Second, we proposed a multiscale feature aggregation module, which deeply fuses salient features at different scales through a feedforward neural network to generate global feature representations with rich semantics, which improves the model's ability to capture image details and represent robust features. Additionally, we designed a semantic-guided region segmentation module, which utilizes a k -modes clustering algorithm to divide the feature map into multiple regions with semantic consistency and performs feature recognition within each semantic region to improve the accuracy of image matching. Finally, we designed a hierarchical reinforcement rotation matching strategy to achieve accurate UAV geolocalization based on the retrieval results of UAV view query satellite images using SuperPoint keypoints extraction and LightGlue rotation matching. According to the experimental results, our method effectively achieves UAV geolocalization.

Post-disaster search and rescue is critical to disaster response and recovery efforts and is often conducted in hazardous and challenging environments. However, the existing post-disaster search and rescue operations have problems such as low efficiency, limited search range, difficulty in identifying the nature of the target, and wrong target location. Therefore, this study develops an air–ground integrated intelligent cognition visual enhancement system based on a UAV (VisionICE). The technique combines a portable AR display device, a camera-equipped helmet, and a quadcopter UAV for efficient patrols over a wide area. First, the system utilizes wireless image sensors on the UAV and helmet to capture images from the air and ground views. Using the YOLOv7 algorithm, the cloud server calculates and analyzes these visual data to accurately identify and detect targets. Lastly, the AR display device obtains real-time intelligent cognitive results. The system allows personnel to simultaneously acquire air and ground dual views and achieve brilliant cognitive results and immersive visual experiences in real time. The findings indicate that the system demonstrates significant recognition accuracy and mobility. In contrast to conventional post-disaster search and rescue operations, the system can autonomously identify and track targets of interest, addressing the difficulty of a person needing help to conduct field inspections in particular environments. At the same time, the system can issue potential threat or anomaly alerts to searchers, significantly enhancing their situational awareness capabilities.

Heterogenous scene matching is one of the key technologies in the field of computer vision. The image rotation problem has been a hot and difficult problem in the field of heterogenous scene matching. In this paper, a heterogenous scene matching method based on the gradient direction distribution field is proposed. The Distributed field theory is introduced into the heterogenous scene matching for the first time. First, the gradient direction distribution field is constructed and fuzzified, and then the effective regions are selected. Then, the concept of main direction distribution field is defined to solve the matching errors due to the existence of rotational transformations between heterogeneous source images. Third, the Chi-square distance is introduced as a similarity measure. At last, the hill-climbing method search strategy is adopted, which greatly improves the efficiency of the algorithm. Experimental results on 8 pairs of infrared and visible heterogenous images demonstrate that the proposed method outperforms the other state-of-the-art region-based matching methods in terms of robustness, accuracy and real-time performance.

Heterogenous scene matching is one of the key technologies in the field of computer vision. The image rotation problem has been a hot and difficult problem in the field of heterogenous scene matching. In this paper, a heterogenous scene matching method based on the gradient direction distribution field is proposed. The Distributed field theory is introduced into the heterogenous scene matching for the first time. First, the gradient direction distribution field is constructed and fuzzified, and then the effective regions are selected. Then, the concept of main direction distribution field is defined to solve the matching errors due to the existence of rotational transformations between heterogeneous source images. Third, the Chi-square distance is introduced as a similarity measure. At last, the hill-climbing method search strategy is adopted, which greatly improves the efficiency of the algorithm. Experimental results on 8 pairs of infrared and visible heterogenous images demonstrate that the proposed method outperforms the other state-of-the-art region-based matching methods in terms of robustness, accuracy and real-time performance.