Aligning Geo-Tagged Clip Representations and Satellite Imagery for Few-Shot Land Use Classification
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| Název: | Aligning Geo-Tagged Clip Representations and Satellite Imagery for Few-Shot Land Use Classification |
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| Autoři: | Jain, Pallavi, Marcos, Diego, Ienco, Dino, Interdonato, Roberto, Dhakal, Aayush, Jacobs, Nathan, Berchoux, Tristan |
| Přispěvatelé: | Blin-Sarah, Sylvie |
| Zdroj: | IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium International Symposium on Geoscience and Remote Sensing (IGARSS 2024) |
| Publication Status: | Preprint |
| Informace o vydavateli: | IEEE, 2024. |
| Rok vydání: | 2024 |
| Témata: | [SDE] Environmental Sciences, Satellite Imagery, Sentinel-2 Imagery, Satellite image, Low resolution, Image classification, Satellite imagery, Model performance, Contrastive learning, Land use classification, Classification task, Satellite data, Land use, Computer vision, Ground-level image |
| Popis: | A major difference between ground-level and satellite imagery of landscapes lies in their semantic granularity: ground-level images tend to offer details on objects and human activities, while satellite images provide broader geographic context but, typically, with coarser semantics. This study aims to leverage this complementary information by integrating fine-grained insights from a ground-level view into the analysis of satellite image data. To achieve this integration, we propose to align a satellite image representation with co-located geo-tagged ground-level image CLIP representations. This method focuses on enriching satellite image visual features by leveraging the inherent visual characteristics found in ground-level images as a reference in a contrastive manner, without relying on additional textual information to guide the learning process. We evaluate the quality of the learned representations on the EuroSAT benchmark in various few-shot settings. |
| Druh dokumentu: | Article Conference object |
| DOI: | 10.1109/igarss53475.2024.10641235 |
| DOI: | 10.5281/zenodo.14196720 |
| DOI: | 10.5281/zenodo.14196719 |
| Přístupová URL adresa: | http://agritrop.cirad.fr/611807/ |
| Rights: | STM Policy #29 CC BY |
| Přístupové číslo: | edsair.doi.dedup.....28042a086a0e92d0cd9b84abcef72c6a |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | A major difference between ground-level and satellite imagery of landscapes lies in their semantic granularity: ground-level images tend to offer details on objects and human activities, while satellite images provide broader geographic context but, typically, with coarser semantics. This study aims to leverage this complementary information by integrating fine-grained insights from a ground-level view into the analysis of satellite image data. To achieve this integration, we propose to align a satellite image representation with co-located geo-tagged ground-level image CLIP representations. This method focuses on enriching satellite image visual features by leveraging the inherent visual characteristics found in ground-level images as a reference in a contrastive manner, without relying on additional textual information to guide the learning process. We evaluate the quality of the learned representations on the EuroSAT benchmark in various few-shot settings. |
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| DOI: | 10.1109/igarss53475.2024.10641235 |