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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| Format: | Article |
| Language: | English |
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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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| author | C. Ayala J. F. Amieva P. Vega R. Perko S. Aleksandrowicz |
| author_facet | C. Ayala J. F. Amieva P. Vega R. Perko S. Aleksandrowicz |
| author_sort | C. Ayala |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d06bffe1fd12470aaa83be0920e7c72a |
| institution | OA Journals |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-d06bffe1fd12470aaa83be0920e7c72a2025-08-20T02:32:22ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-05-01XLVIII-M-6-2025616610.5194/isprs-archives-XLVIII-M-6-2025-61-2025Tiny Object Detection in Super-Resolved Sentinel-2 ImageryC. Ayala0J. F. Amieva1P. Vega2R. Perko3S. Aleksandrowicz4Tracasa Instrumental, Calle Cabárceno 6, 31621 Sarriguren, SpainTracasa Instrumental, Calle Cabárceno 6, 31621 Sarriguren, SpainTracasa Instrumental, Calle Cabárceno 6, 31621 Sarriguren, SpainJOANNEUM RESEARCH, Leonhardstrasse 59, 8010 Graz, AustriaSpace Research Centre of the Polish Academy of Sciences - Bartycka 18A, 00-716 Wasaw, PolandThe 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.https://isprs-archives.copernicus.org/articles/XLVIII-M-6-2025/61/2025/isprs-archives-XLVIII-M-6-2025-61-2025.pdf |
| spellingShingle | C. Ayala J. F. Amieva P. Vega R. Perko S. Aleksandrowicz Tiny Object Detection in Super-Resolved Sentinel-2 Imagery The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Tiny Object Detection in Super-Resolved Sentinel-2 Imagery |
| title_full | Tiny Object Detection in Super-Resolved Sentinel-2 Imagery |
| title_fullStr | Tiny Object Detection in Super-Resolved Sentinel-2 Imagery |
| title_full_unstemmed | Tiny Object Detection in Super-Resolved Sentinel-2 Imagery |
| title_short | Tiny Object Detection in Super-Resolved Sentinel-2 Imagery |
| title_sort | tiny object detection in super resolved sentinel 2 imagery |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-M-6-2025/61/2025/isprs-archives-XLVIII-M-6-2025-61-2025.pdf |
| work_keys_str_mv | AT cayala tinyobjectdetectioninsuperresolvedsentinel2imagery AT jfamieva tinyobjectdetectioninsuperresolvedsentinel2imagery AT pvega tinyobjectdetectioninsuperresolvedsentinel2imagery AT rperko tinyobjectdetectioninsuperresolvedsentinel2imagery AT saleksandrowicz tinyobjectdetectioninsuperresolvedsentinel2imagery |