Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations
This study explores the potential of integrating multi-source remote sensing data—including Sentinel-1 synthetic aperture radar (SAR) imagery, Sentinel-2 optical imagery, and Landsat 8 thermal data—for crop classification in Emilia-Romagna (Northern Italy). Using satellite imager...
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Copernicus Publications
2025-07-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1237/2025/isprs-archives-XLVIII-G-2025-1237-2025.pdf |
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| author | Y. Qi E. Mandanici M. Helmy F. Trevisiol G. Bitelli |
| author_facet | Y. Qi E. Mandanici M. Helmy F. Trevisiol G. Bitelli |
| author_sort | Y. Qi |
| collection | DOAJ |
| description | This study explores the potential of integrating multi-source remote sensing data—including Sentinel-1 synthetic aperture radar (SAR) imagery, Sentinel-2 optical imagery, and Landsat 8 thermal data—for crop classification in Emilia-Romagna (Northern Italy). Using satellite imagery and agricultural surveys, we constructed a temporal dataset covering 2020 with 27 biweekly time steps. After filtering out underrepresented crop types with insufficient samples for machine learning training, nine crop types remained. We implemented four deep learning models using TensorFlow: Dense Neural Network (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Transformer. Our results indicate that removing underrepresented crops significantly improves classification performance, leading to an overall accuracy of approximately 91%. Incorporating Landsat 8 thermal data further enhanced accuracy, with the Transformer model achieving a peak accuracy of 92.08%. A crop-specific analysis revealed that temperature observations notably improved classification for crops with distinct thermal signatures (e.g., sugar beets, corn), whereas limited improvement was observed for spectrally similar cereals (e.g., wheat, barley). Overall, the Transformer model demonstrated exceptional ability in capturing spatial-temporal dependencies in multivariate time-series data. These findings underscore the advantages of integrating multi-source satellite data including thermal infrared and leveraging attention-based neural networks for large-scale agricultural monitoring and resource management. |
| format | Article |
| id | doaj-art-295a721a00c844c6b2b8da237d647f55 |
| institution | Kabale University |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-295a721a00c844c6b2b8da237d647f552025-08-20T03:58:32ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-07-01XLVIII-G-20251237124510.5194/isprs-archives-XLVIII-G-2025-1237-2025Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal ObservationsY. Qi0E. Mandanici1M. Helmy2F. Trevisiol3G. Bitelli4Dept. of Civil, Chemical, Environmental and Materials Engineering (DICAM), Survey and Geomatics Laboratory (LARIG), University of Bologna, Bologna, ItalyDept. of Civil, Chemical, Environmental and Materials Engineering (DICAM), Survey and Geomatics Laboratory (LARIG), University of Bologna, Bologna, ItalyDept. of Civil, Chemical, Environmental and Materials Engineering (DICAM), Survey and Geomatics Laboratory (LARIG), University of Bologna, Bologna, ItalyCIMA Research Foundation, Via A. Magliotto 2, 17100 Savona, ItalyDept. of Civil, Chemical, Environmental and Materials Engineering (DICAM), Survey and Geomatics Laboratory (LARIG), University of Bologna, Bologna, ItalyThis study explores the potential of integrating multi-source remote sensing data—including Sentinel-1 synthetic aperture radar (SAR) imagery, Sentinel-2 optical imagery, and Landsat 8 thermal data—for crop classification in Emilia-Romagna (Northern Italy). Using satellite imagery and agricultural surveys, we constructed a temporal dataset covering 2020 with 27 biweekly time steps. After filtering out underrepresented crop types with insufficient samples for machine learning training, nine crop types remained. We implemented four deep learning models using TensorFlow: Dense Neural Network (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Transformer. Our results indicate that removing underrepresented crops significantly improves classification performance, leading to an overall accuracy of approximately 91%. Incorporating Landsat 8 thermal data further enhanced accuracy, with the Transformer model achieving a peak accuracy of 92.08%. A crop-specific analysis revealed that temperature observations notably improved classification for crops with distinct thermal signatures (e.g., sugar beets, corn), whereas limited improvement was observed for spectrally similar cereals (e.g., wheat, barley). Overall, the Transformer model demonstrated exceptional ability in capturing spatial-temporal dependencies in multivariate time-series data. These findings underscore the advantages of integrating multi-source satellite data including thermal infrared and leveraging attention-based neural networks for large-scale agricultural monitoring and resource management.https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1237/2025/isprs-archives-XLVIII-G-2025-1237-2025.pdf |
| spellingShingle | Y. Qi E. Mandanici M. Helmy F. Trevisiol G. Bitelli Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations |
| title_full | Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations |
| title_fullStr | Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations |
| title_full_unstemmed | Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations |
| title_short | Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations |
| title_sort | enhancing crop classification in emilia romagna italy using transformer based multi source data fusion with thermal observations |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1237/2025/isprs-archives-XLVIII-G-2025-1237-2025.pdf |
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