Szczegóły publikacji
Opis bibliograficzny
UNet neural network in agricultural land cover classification using Sentinel-2 / P. KRAMARCZYK, B. HEJMANOWSKA // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; ISSN 1682-1750. — 2023 — vol. 48-1/W3, s. 85-90. — Bibliogr. s. 90, Abstr. — Publikacja dostępna online od: 2023-10-19. — 2nd GEOBENCH workshop on Evaluation and BENCHmarking of sensors, systems and GEOspatial data in photogrammetry and remote sensing : 23–24 October 2023, Krakow, Poland
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 152012 |
|---|---|
| Data dodania do BaDAP | 2024-04-05 |
| Tekst źródłowy | URL |
| DOI | 10.5194/isprs-archives-XLVIII-1-W3-2023-85-2023 |
| Rok publikacji | 2023 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Abstract
The article discusses a method for classifying land cover types in rural areas using a trained neural network. The focus is on distinguishing agriculturally cultivated areas and differentiating bare soil from quarry areas. This distinction is not present in publicly available databases like CORINE, UrbanAtlas, EuroSAT, or BigEarthNet. The research involves training a neural network on multi-temporal patches to classify Sentinel-2 images rapidly. This approach allows automated monitoring of cultivated areas, determining periods of bare soil vulnerability to erosion, and identifying open-pit areas with similar spectral characteristics to bare soil. After training the U-Net network, it achieved an average classification accuracy of 90% (OA) in the test areas, highlighting the importance of using OA for multi-class classifications, instead of ACC. Analysis of our main classes revealed high accuracy, 99.01% for quarries, 92.3% for bare soil, and an average of 94.8% for annual crops, demonstrating the model's capability to differentiate between crops at various growth stages and assess land cover categories effectively.