A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features
Land cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep learning (DL), particularly convolutional neural networks (CNNs) and vision transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this wor...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10948270/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850173833682616320 |
|---|---|
| author | Luigi Russo Antonietta Sorriso Silvia Liberata Ullo Paolo Gamba |
| author_facet | Luigi Russo Antonietta Sorriso Silvia Liberata Ullo Paolo Gamba |
| author_sort | Luigi Russo |
| collection | DOAJ |
| description | Land cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep learning (DL), particularly convolutional neural networks (CNNs) and vision transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformer-based Swin-Unet architecture with seasonal synthesized spatio-temporal images has been employed to classify LC types using spatio-temporal features extracted from Sentinel-1 (S1) synthetic aperture radar (SAR) data, organized into seasonal clusters. The study focuses on three distinct regions—Amazonia, Africa, and Siberia—and evaluates the model performance across diverse ecoregions within these areas. By utilizing seasonal feature sequences instead of dense temporal sequences, notable performance improvements have been achieved, especially in regions with temporal data gaps such as Siberia, where S1 data distribution is uneven and nonuniform. The results demonstrate the effectiveness and the generalization capabilities of the proposed methodology in achieving high overall accuracy (OA) values, even in regions with limited training data. |
| format | Article |
| id | doaj-art-3efae2d148b24d12b30502bb7b687fda |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-3efae2d148b24d12b30502bb7b687fda2025-08-20T02:19:47ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118105621058110.1109/JSTARS.2025.355768710948270A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 FeaturesLuigi Russo0https://orcid.org/0009-0001-3659-0087Antonietta Sorriso1https://orcid.org/0009-0005-9892-2445Silvia Liberata Ullo2https://orcid.org/0000-0001-6294-0581Paolo Gamba3https://orcid.org/0000-0002-9576-6337Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, ItalyDepartment of Engineering, University of Sannio, Benevento, ItalyDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, ItalyLand cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep learning (DL), particularly convolutional neural networks (CNNs) and vision transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformer-based Swin-Unet architecture with seasonal synthesized spatio-temporal images has been employed to classify LC types using spatio-temporal features extracted from Sentinel-1 (S1) synthetic aperture radar (SAR) data, organized into seasonal clusters. The study focuses on three distinct regions—Amazonia, Africa, and Siberia—and evaluates the model performance across diverse ecoregions within these areas. By utilizing seasonal feature sequences instead of dense temporal sequences, notable performance improvements have been achieved, especially in regions with temporal data gaps such as Siberia, where S1 data distribution is uneven and nonuniform. The results demonstrate the effectiveness and the generalization capabilities of the proposed methodology in achieving high overall accuracy (OA) values, even in regions with limited training data.https://ieeexplore.ieee.org/document/10948270/Convolutional neural network (CNN)deep learning (DL)land cover (LC) mappingneural networkSentinel-1 (S1)swin-Unet |
| spellingShingle | Luigi Russo Antonietta Sorriso Silvia Liberata Ullo Paolo Gamba A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolutional neural network (CNN) deep learning (DL) land cover (LC) mapping neural network Sentinel-1 (S1) swin-Unet |
| title | A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features |
| title_full | A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features |
| title_fullStr | A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features |
| title_full_unstemmed | A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features |
| title_short | A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features |
| title_sort | deep learning architecture for land cover mapping using spatio temporal sentinel 1 features |
| topic | Convolutional neural network (CNN) deep learning (DL) land cover (LC) mapping neural network Sentinel-1 (S1) swin-Unet |
| url | https://ieeexplore.ieee.org/document/10948270/ |
| work_keys_str_mv | AT luigirusso adeeplearningarchitectureforlandcovermappingusingspatiotemporalsentinel1features AT antoniettasorriso adeeplearningarchitectureforlandcovermappingusingspatiotemporalsentinel1features AT silvialiberataullo adeeplearningarchitectureforlandcovermappingusingspatiotemporalsentinel1features AT paologamba adeeplearningarchitectureforlandcovermappingusingspatiotemporalsentinel1features AT luigirusso deeplearningarchitectureforlandcovermappingusingspatiotemporalsentinel1features AT antoniettasorriso deeplearningarchitectureforlandcovermappingusingspatiotemporalsentinel1features AT silvialiberataullo deeplearningarchitectureforlandcovermappingusingspatiotemporalsentinel1features AT paologamba deeplearningarchitectureforlandcovermappingusingspatiotemporalsentinel1features |