Mr. Xiaofei Hu Profile

Xiaofei Hu

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Proceedings Article | 16 May 2024 Paper

Street-to-satellite view synthesis for cross-view geo-localization

Luying Zhao, Yang Zhou, Xiaofei Hu, Wenjian Gan, Gaoshuang Huang, Chenglong Zhang, Mingbo Hou

Proceedings Volume 13166, 1316608 (2024) https://doi.org/10.1117/12.3029089

KEYWORDS: Satellite imaging, Panoramic photography, Image processing, Image retrieval

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Cross-view geolocation, which aims to geolocate ground-view images using reference satellite imagery, is a challenging task that requires effective strategies to minimize significant disparities between images. In this paper, we propose a novel approach to address this challenge. By exploiting the projection relationship between ground images and satellite images, we are able to convert ground-view images into satellite-view images, thereby mitigating the inherent disparities between the two perspectives. To enhance the converted images further, we employ a conditional generative adversarial network (CGAN). This network generates satellite perspective images that exhibit greater consistency with the actual satellite data, thereby reducing excessive disparities and improving overall image quality. Additionally, we adopt a joint training approach in a multi-task setting, wherein we synthesize satellite images from ground images and conduct cross-view image matching. This framework facilitates mutual learning between the tasks and leads to improved performance. Empirical evaluation of our proposed method showcases significant advancements in terms of retrieval accuracy and synthesis quality when compared to existing techniques. These findings underscore the potential of our approach in addressing the challenges associated with cross-view geolocation.

Proceedings Article | 21 February 2024 Paper

Visualization and visual analysis of cyberspace metaphors based on continuous cartogram

Longhui Liu, Yang Zhou, Qunshan Shi, Xiaofei Hu, Shihao Shi

Proceedings Volume 12988, 129881I (2024) https://doi.org/10.1117/12.3024098

KEYWORDS: Visualization, Visual analytics, Internet, Data modeling

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Cyberspace mapping has emerged as a new and compelling area of research within the field of cartography. This paper addresses the challenge of overlapping points in cyberspace visualization, which poses difficulties for visual analysis. To tackle this issue, we propose a cyberspace metaphorical cartogram map that combines metaphorical mapping and continuous cartogram technology. Additionally, based on our method, we use our method to construct a cyberspace map using global IPv6 data from the Asia-Pacific Network Information Center (APNIC) to analyze the current development pattern of the global IPv6 network. Experimental results demonstrate the effectiveness of our method in avoiding node duplication, aligning with the reading logic of geospatial maps, and providing an efficient visualization approach for data comprehension.

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