Diffusion models for remote sensing imagery semantic segmentation
| dc.contributor.author | Ayala Lauroba, Christian | |
| dc.contributor.author | Sesma Redín, Rubén | |
| dc.contributor.author | Aranda, Carlos | |
| dc.contributor.author | Galar Idoate, Mikel | |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.funder | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA25-2022 | |
| dc.date.accessioned | 2024-10-09T09:58:02Z | |
| dc.date.available | 2024-10-09T09:58:02Z | |
| dc.date.issued | 2023-10-20 | |
| dc.date.updated | 2024-10-09T09:50:33Z | |
| dc.description.abstract | Denoising Diffusion Probabilistic Models have exhibited impressive performance for generative modelling of images. This paper aims to explore the potential of diffusion models for semantic segmentation tasks in the context of remote sensing. The major challenge of employing these models for semantic segmentation tasks is the generative nature of the model, which produces an arbitrary segmentation mask from a random noise input. Therefore, the diffusion process needs to be constrained to produce a segmentation mask that matches the target image. To address this issue, the denoising process is conditioned by utilizing the input image as a reference. In the experimental study, the proposed model is compared against other state-of-the-art semantic segmentation architectures using the Massachusetts Buildings Aerial dataset. The results of this study provide valuable insights into the potential of diffusion models for semantic segmentation tasks in the field of remote sensing. | en |
| dc.description.sponsorship | Thanks to the Government of Navarre for supporting under the industrial PhD program 2020 reference 0011-1408-2020-000008 Thanks to the Spanish Ministry of Science and Innovation for supporting under project PID2019-108392GB-I00 (AEI/10.13039/501100011033) and the Public University of Navarre under project PJUPNA25-2022. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Ayala, C., Sesma, R., Aranda, C., Galar, M. (2023) Diffusion models for remote sensing imagery semantic segmentation. In [IEEE], 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5654-5657). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS52108.2023.10281461 | |
| dc.identifier.doi | 10.1109/IGARSS52108.2023.10281461 | |
| dc.identifier.isbn | 979-8-3503-2010-7 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/52122 | |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.relation.ispartof | In [IEEE]. 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Piscataway: Institute of Electrical and Electronics Engineers Inc.; 2023. p. 5654-5657 | |
| 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.publisherversion | https://doi.org/10.1109/IGARSS52108.2023.10281461 | |
| dc.rights | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Building segmentation | en |
| dc.subject | Denoising diffusion probabilistic models | en |
| dc.subject | Remote sensing | en |
| dc.subject | Semantic segmentation | en |
| dc.subject | Uncertainty estimation | en |
| dc.title | Diffusion models for remote sensing imagery semantic segmentation | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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