Mingbo Hou

Narrow field-of-view (NFoV) image cross-view localization is limited by insufficient information in the ground scene itself and cross-view domain differences, resulting in even more limited information directly corresponding to satellite imagery of the same area. This leads to low accuracy in existing methods and relatively few related studies. To address this issue, this paper innovatively employs cube projection technology to construct an NFoV ground dataset. Compared to traditional panoramic cropping methods, this method converts cylindrical projection images into central projection images, accurately simulating camera imaging geometry and providing high-fidelity data support. Secondly, this paper introduces monocular depth estimation to reduce interference information from ground images during satellite image retrieval, thereby highlighting core localization features. Therefore, a two-branch ground feature extraction algorithm (processing the original image and enhancing the image after filtering out invalid information) is designed to mine rich ground features, supplemented by an independent satellite feature extraction branch, forming a three-branch network to improve the accuracy of cross-view localization in narrow field-of-view images. Experimental results show that on the CVUSA dataset, the TOP-1 localization accuracy with a 90-degree field of view is improved from 55.9% to 89.14%; and on the CVACT dataset, the TOP-1 localization accuracy with a narrow field of view is improved from 40.6% to 84.6%.

Cross-view image geo-localization refers to determining the geographic location of a given query image using an image database with known location information. Existing methods mainly focus on learning discriminative image representations to optimize the distance of image feature representations in feature space without fully considering the positional relation information of the features and the information redundancy in the features themselves. Therefore, we proposed a cross-view image localization method that combines global spatial relation attention with feature aggregation. First, we utilize the lightweight global spatial relation attention to learn the spatial location structure information of features, which fully enhances the perceptual and discriminative capabilities of the model. The proposed attention has little effect on the complexity and memory occupancy of the model and can be generalized to other image-processing tasks. In addition, we introduce the sinkhorn algorithm for locally aggregated descriptors, which represents the aggregation of local features as an optimal transport problem and selectively discards useless information during the clustering and assignment of features, thus enhancing the generalization and robustness of the descriptors. Experimental results on the public University-1652, CVACT, and CVUSA datasets validate the effectiveness and superiority of the proposed method. Our code is available at https://github.com/WenjianGan/LRFR.

Geolocating a street-view image by matching it with geotagged satellite images is crucial for location assessment. However, the perspective disparity between satellite and street-view images presents significant challenges. To address the issue, the mainstream approach is to convert satellite images to ground-level perspective. So, the reference satellite images not only required the center but also required the coverage to be consistent with the street-view images and sometimes even required consistency in the north direction, which is difficult to achieve in the practical applications. This letter introduces a ground-breaking method for converting ground-level images to satellite images. We effectively couple ground images and satellite images by establishing a hemispheric projection relationship to achieve conversion from ground images to satellite perspectives, thus solving the problem of huge perspective differences in cross-view geolocation (CVG). In addition, we propose the multiscale fusion of a spatial features mechanism to enhance deep feature representations and improve recall and geolocation accuracy. Our proposed methodology markedly enhances its practical utility and performance, attaining remarkable Top-1 accuracy rates of 75.08% on the CVACT_val dataset and 39.92% on the CVACT_test dataset, respectively, following a stringent quantitative appraisal. This advancement constitutes a substantial contribution to the progression of research in the field of CVG.