The accurate detection and extraction of roads using remote sensing technology are crucial to the development of the transportation industry and intelligent perception tasks. Recently, in view of the advantages of CNNs in feature extraction, its related road extraction methods have been proposed successively. However, due to the limitation of kernel size, they perform less effectively at capturing long-range information and global context, which are crucial for road targets distributed over long distances and highly structured. To deal with this problem, a novel model named RoadFormer with a Swin Transformer as the backbone is developed in this paper. Firstly, to extract long-range information effectively, a Swin Transformer multi-scale encoder is adopted in our model. Secondly, to enhance the feature representation capability of the model, we design an innovative bottleneck module, in which the spatial and channel separable convolution is employed to obtain fine-grained and globe features, and then a dilated block is connected after the spatial convolution module to capture more integrated road structures. Finally, a lightweight decoder consisting of transposed convolution and skip connection generates the final extraction results. Extensive experimental results confirm the advantages of RoadFormer on the Deepglobe and Massachusetts datasets. The comparative results of visualization and quantification demonstrate that our model outperforms comparable methods.

Cross-view geo-localization via scene matching is crucial in unmanned aerial vehicle (UAV) systems in global navigation satellite system (GNSS) denial environment. However, images in the same scene may undergo geometric distortion and occlusion due to difference in capture viewpoint, time and platform. The existing methods mainly extract consistent features between images by CNNs, while ignoring the semantic distribution and structural information of the objects. Aiming at addressing this issue, we introduce a semantic-aware graph convolutional network (SeGCN). To improve consistent representation of object features from different viewpoints, potential semantic features are inferred via cross-attention of image context. Then, for exploring the structural information of objects, SeGCN performs graph convolution on graph structures constructed from the same semantic features. Finally, the composite features generated by SeGCN and backbone are utilized for scene matching. Comprehensive experiments conducted on the University-1652 and SUES-200 benchmarks establish that the proposed approach attains the highest levels of accuracy in both localization and navigation tasks. Furthermore, we conducted localization simulation experiments on our real UAV datasets, confirming the effectiveness of SeGCN in real world application scenarios.

Current CNN-based methods for infrared and visible image fusion are limited by the low discrimination of extracted structural features, the adoption of uniform loss functions, and the lack of inter-modal feature interaction, which make it difficult to obtain optimal fusion results. To alleviate the above problems, a framework for multimodal feature learning fusion using a cross-attention Transformer is proposed. To extract rich structural features at different scales, residual U-Nets with mixed receptive fields are adopted to capture salient object information at various granularities. Then, a hybrid attention fusion strategy is employed to integrate the complementing information from the input images. Finally, adaptive loss functions are designed to achieve optimal fusion results for different modal features. The fusion framework proposed in this study is thoroughly evaluated using the TNO, FLIR, and LLVIP datasets, encompassing diverse scenes and varying illumination conditions. In the comparative experiments, HATF achieved competitive results on three datasets, with EN, SD, MI, and SSIM metrics reaching the best performance on the TNO dataset, surpassing the second-best method by 2.3%, 18.8%, 4.2%, and 2.2%, respectively. These results validate the effectiveness of the proposed method in terms of both robustness and image fusion quality compared to several popular methods.