National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets

National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets

Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extrac...

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Dades bibliogràfiques
Autors principals:	Junshi Xia, Naoto Yokoya, Bruno Adriano, Keiichiro Kanemoto
Format:	Article
Idioma:	English
Publicat:	Elsevier 2023-03-01
Col·lecció:	International Journal of Applied Earth Observations and Geoinformation
Matèries:	Cropland mapping Agricultural census Multi-temporal Multi-modality Random forests
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S1569843223000158

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