MMHCA: Multi-feature representations based on multi-scale hierarchical contextual aggregation for UAV-view geo-localization

• A strong DiNAT-based baseline is proposed that captures more global context and exponentially expands the receptive fields, making it more competitive with CNN-based frameworks and traditional Transformer structures. • The multi-scale hierarchical contextual aggregation method is designed to extract contextual information from the local to global of the input at different granularity levels, which enables the extracted contextual information to be effectively associated with local and global information in the image. • The multi-feature representations method is designed which utilizes techniques such as gated aggregation to obtain rich feature representations with discrimination. • The proposed MMHCA method achieves excellent performance on the University-1652 and SUES-200. The superiority and effectiveness of the MMHCA in UAV-view target localization and UAV navigation tasks have been confirmed through comparative experiments and ablation studies. In global navigation satellite system denial environment, cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle (UAV) systems. The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms, such as UAV-view and satellite-view images. However, images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform, view, and timing. The existing methods predominantly extract features by segmenting feature maps, which overlook the holistic semantic distribution and structural information of objects, resulting in loss of image information. To address these challenges, dilated neighborhood attention Transformer is employed as the feature extraction backbone, and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation (MMHCA) is proposed. In the proposed MMHCA method, the multi-scale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels, establishing feature associations of contextual information with global and local information in the image. Subsequently, the multi-feature representations method is utilized to obtain rich discriminative feature information, bolstering the robustness of model in scenarios characterized by positional shifts, varying distances, and scale ambiguities. Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques, showing outstanding results in UAV localization and navigation.