Dual-Context Joint Representation for Near-Infrared Geo-Localization in Urban Environments

Abstract

Visual geo-localization seeks to enable autonomous aircraft positioning in GNSS-denied environments through large-scale image retrieval. Most existing methods, however, primarily rely on visible light images and are thus inherently sensitive to illumination changes and adverse weather conditions. Near-infrared (NIR) images offer superior environmental robustness but lack well-established benchmarks and specialized retrieval algorithms. To bridge this gap, we introduce NIR-cities, the first urban-scale benchmark for NIR aerial-to-satellite geo-localization. Unlike previous datasets, it features unaligned queries and continuous spatial coverage, simulating nadir-view aerial queries. Building on this, we propose a novel dual-context joint representation (DCJR) network specifically designed for NIR images. DCJR decouples the image representation into salient and environmental contexts via saliency-guided patch encoding (SPE). A dual-context enhancement (DCE) module then adaptively fuses these dual cues to address local ambiguities. Finally, region-prototype aggregation (RPA) distills the features through learnable prototypes, resulting in a discriminative and compact global descriptor. Extensive experiments demonstrate that DCJR significantly outperforms state-of-the-art methods, attaining a remarkable R@1 of 73.44% while achieving a favorable accuracy-efficiency tradeoff.