Multi-temporal data augmentation for high frequency satellite imagery: a case study in Sentinel-1 and Sentinel-2 building and road segmentation
| dc.contributor.author | Ayala Lauroba, Christian | |
| dc.contributor.author | Aranda Magallón, Coral | |
| dc.contributor.author | Galar Idoate, Mikel | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.date.accessioned | 2023-05-30T09:38:56Z | |
| dc.date.available | 2023-05-30T09:38:56Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-05-30T08:29:54Z | |
| dc.description.abstract | Semantic segmentation of remote sensing images has many practical applications such as urban planning or disaster assessment. Deep learning-based approaches have shown their usefulness in automatically segmenting large remote sensing images, helping to automatize these tasks. However, deep learning models require large amounts of labeled data to generalize well to unseen scenarios. The generation of global-scale remote sensing datasets with high intraclass variability presents a major challenge. For this reason, data augmentation techniques have been widely applied to artificially increase the size of the datasets. Among them, photometric data augmentation techniques such as random brightness, contrast, saturation, and hue have been traditionally applied aiming at improving the generalization against color spectrum variations, but they can have a negative effect on the model due to their synthetic nature. To solve this issue, sensors with high revisit times such as Sentinel-1 and Sentinel-2 can be exploited to realistically augment the dataset. Accordingly, this paper sets out a novel realistic multi-temporal color data augmentation technique. The proposed methodology has been evaluated in the building and road semantic segmentation tasks, considering a dataset composed of 38 Spanish cities. As a result, the experimental study shows the usefulness of the proposed multi-temporal data augmentation technique, which can be further improved with traditional photometric transformations. | en |
| dc.description.sponsorship | Christian Ayala was partially supported by the Government of Navarra under the industrial Ph.D. program 2020 reference 0011-1408-2020-000008. Mikel Galar was partially supported by Tracasa Instrumental S.L. under the project OTRI 2020- 901-156, and by the Spanish MICIN (PID2019-108392GB-I00 / AEI / 10.13039/501100011033). | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Ayala, C., Aranda, C., & Galar, M. (2022). Multi-temporal data augmentation for high frequency satellite imagery: A case study in sentinel-1 and sentinel-2 building and road segmentation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 25-32. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-25-2022 | en |
| dc.identifier.doi | 10.5194/isprs-archives-XLIII-B3-2022-25-2022 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/45358 | |
| dc.language.iso | eng | en |
| dc.publisher | ISPRS | en |
| dc.relation.ispartof | The international archives of the photogrammetry, remote sensing and spatial information sciences. XXIV ISPRS Congress: ISPRS; 2022. p.25-32 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra//0011-1408-2020-000008/ | |
| dc.relation.publisherversion | https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-25-2022 | |
| dc.rights | © Author(s) 2022. CC BY 4.0 License. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Sentinel-2 | en |
| dc.subject | Sentinel-1 | en |
| dc.subject | Multi-temporal | en |
| dc.subject | Remote sensing | en |
| dc.subject | Road network extraction | en |
| dc.subject | Building footprint detection | en |
| dc.subject | Deep learning | en |
| dc.subject | Convolutional neural networks | en |
| dc.title | Multi-temporal data augmentation for high frequency satellite imagery: a case study in Sentinel-1 and Sentinel-2 building and road segmentation | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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