Enhancing anomaly detection in satellite imagery using self-supervised learning techniques.
Saved in:
| Title: | Enhancing anomaly detection in satellite imagery using self-supervised learning techniques. |
|---|---|
| Authors: | Barba-Rojas, Raúl1 (AUTHOR) raul.barba@ubotica.com, Espinosa-Aranda, Jose Luis1 (AUTHOR) josel.espinosa@ubotica.com, Díez, Jorge2 (AUTHOR) jdiez@uniovi.es |
| Source: | Neural Computing & Applications. Jan2026, Vol. 38 Issue 2, p1-22. 22p. |
| Subjects: | Remote-sensing images, Remote sensing, Machine learning, Metadata, Ensemble learning, Outlier detection |
| Abstract: | Satellite imagery provides society with data to solve a wide range of problems, including monitoring environmental changes or the detection of objects of interest, among many others. Anomaly detection has gained attention in recent years, as it can be used to address the aforementioned problems effectively. Traditional anomaly detection methods pose challenges, as they rely on labelled datasets, which are often scarce, expensive to obtain, and limited in capturing the full spectrum of real-world anomalies. To address these challenges, self-supervised learning has emerged as a powerful paradigm that leverages the usage of anomaly detection techniques on unlabelled data, allowing for cheaper solutions that can learn robust representations of known and unknown anomalies in many fields, including earth observation-related fields. In this study, we investigate state-of-the-art self-supervised anomaly detection methods and assess their performance on two widely used satellite imagery datasets: LandCover.ai, which focuses on land cover classification, and the High-Resolution Cloud Detection Dataset, which provides detailed cloud cover annotations. These datasets enable a comprehensive evaluation of anomaly detection effectiveness across diverse geospatial contexts. Nevertheless, existing state-of-the-art models often exhibit limitations, particularly in achieving a balanced trade-off between precision and recall, largely due to the scarcity of supervisory information inherent to the anomaly detection task. To address this challenge, we propose a novel ensemble self-supervised learning framework that integrates both low- and high-level abstraction objectives, thereby leveraging local and global feature representations to improve anomaly detection performance. Experimental results demonstrate that our approach consistently outperforms existing self-supervised methods, achieving improvements in F1 score ranging from 2 to 10% across both datasets. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 191134281 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Enhancing anomaly detection in satellite imagery using self-supervised learning techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Barba-Rojas%2C+Raúl%22">Barba-Rojas, Raúl</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> raul.barba@ubotica.com</i><br /><searchLink fieldCode="AR" term="%22Espinosa-Aranda%2C+Jose+Luis%22">Espinosa-Aranda, Jose Luis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> josel.espinosa@ubotica.com</i><br /><searchLink fieldCode="AR" term="%22Díez%2C+Jorge%22">Díez, Jorge</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jdiez@uniovi.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jan2026, Vol. 38 Issue 2, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Satellite imagery provides society with data to solve a wide range of problems, including monitoring environmental changes or the detection of objects of interest, among many others. Anomaly detection has gained attention in recent years, as it can be used to address the aforementioned problems effectively. Traditional anomaly detection methods pose challenges, as they rely on labelled datasets, which are often scarce, expensive to obtain, and limited in capturing the full spectrum of real-world anomalies. To address these challenges, self-supervised learning has emerged as a powerful paradigm that leverages the usage of anomaly detection techniques on unlabelled data, allowing for cheaper solutions that can learn robust representations of known and unknown anomalies in many fields, including earth observation-related fields. In this study, we investigate state-of-the-art self-supervised anomaly detection methods and assess their performance on two widely used satellite imagery datasets: LandCover.ai, which focuses on land cover classification, and the High-Resolution Cloud Detection Dataset, which provides detailed cloud cover annotations. These datasets enable a comprehensive evaluation of anomaly detection effectiveness across diverse geospatial contexts. Nevertheless, existing state-of-the-art models often exhibit limitations, particularly in achieving a balanced trade-off between precision and recall, largely due to the scarcity of supervisory information inherent to the anomaly detection task. To address this challenge, we propose a novel ensemble self-supervised learning framework that integrates both low- and high-level abstraction objectives, thereby leveraging local and global feature representations to improve anomaly detection performance. Experimental results demonstrate that our approach consistently outperforms existing self-supervised methods, achieving improvements in F1 score ranging from 2 to 10% across both datasets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191134281 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-025-11746-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Remote-sensing images Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Metadata Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Outlier detection Type: general Titles: – TitleFull: Enhancing anomaly detection in satellite imagery using self-supervised learning techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Barba-Rojas, Raúl – PersonEntity: Name: NameFull: Espinosa-Aranda, Jose Luis – PersonEntity: Name: NameFull: Díez, Jorge IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 38 – Type: issue Value: 2 Titles: – TitleFull: Neural Computing & Applications Type: main |
| ResultId | 1 |
