Tiny Object Detection in Super-Resolved Sentinel-2 Imagery
The detection of tiny objects in satellite imagery is a critical task with wide-ranging applications, including environmental monitoring, urban planning, disaster response, and the surveillance of critical transport infrastructure. Sentinel-2 satellite data, characterized by providing rich spectral...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-M-6-2025/61/2025/isprs-archives-XLVIII-M-6-2025-61-2025.pdf |
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| Summary: | The detection of tiny objects in satellite imagery is a critical task with wide-ranging applications, including environmental monitoring, urban planning, disaster response, and the surveillance of critical transport infrastructure. Sentinel-2 satellite data, characterized by providing rich spectral information at a moderate spatial resolution (10–60m), poses significant challenges for the identification of small-scale features due to limited spatial detail and the effects of mixed pixels. This study investigates the potential of super-resolution techniques to enhance Sentinel-2 imagery for improved tiny object detection. A dataset was meticulously annotated to identify aircraft across diverse areas of interest, enabling rigorous evaluation using advanced methodologies. Detection was performed using a hybrid approach that combines a YOLOv8-based object detector and a vision-transformer-based object density estimator. The fusion of these complementary methods significantly reduces false positives, resulting in improvements in precision and F1 score. The findings underscore that super-resolved Sentinel-2 imagery offers a viable and cost-effective solution for detecting tiny objects, particularly in scenarios where access to high-resolution imagery is restricted or economically prohibitive. |
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| ISSN: | 1682-1750 2194-9034 |