دورية أكاديمية
Transfer learning approach based on satellite image time series for the crop classification problem
| العنوان: | Transfer learning approach based on satellite image time series for the crop classification problem |
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| المؤلفون: | Ognjen Antonijević, Slobodan Jelić, Branislav Bajat, Milan Kilibarda |
| المصدر: | Journal of Big Data, Vol 10, Iss 1, Pp 1-19 (2023) |
| بيانات النشر: | SpringerOpen, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Information technology LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Transfer learning, Remote sensing, Encoder–decoder architecture, Domain adaptation, Crop classification, Attention mechanism, Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Abstract This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During preprocessing, cloudy images are removed from the input data, the time series are interpolated over the time dimension, and additional remote sensing indices are calculated. We chose TransformerEncoder as the base model for knowledge transfer from source to target domain with French and Serbian data, respectively. Even more, the accuracy of the base model with the preprocessing step is improved by 2% when trained and evaluated on the French dataset. The transfer learning approach with fine-tuning of the pre-trained weights on the French dataset outperformed all other methods in terms of overall accuracy 0.94 and mean class recall 0.907 on the Serbian dataset. Our partially fine-tuned model improved recall of crop types that were poorly classified by the base model. In the case of sugar beet, class recall is improved by 85.71%. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2196-1115 |
| Relation: | https://doaj.org/toc/2196-1115 |
| DOI: | 10.1186/s40537-023-00735-2 |
| URL الوصول: | https://doaj.org/article/c60d8c8763d64ca4ba34dc266eac5446 |
| رقم الأكسشن: | edsdoj.60d8c8763d64ca4ba34dc266eac5446 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 21961115 |
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| DOI: | 10.1186/s40537-023-00735-2 |