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...

Полное описание

Библиографические подробности
Главные авторы:	Junshi Xia, Naoto Yokoya, Bruno Adriano, Keiichiro Kanemoto
Формат:	Статья
Язык:	English
Опубликовано:	Elsevier 2023-03-01
Серии:	International Journal of Applied Earth Observations and Geoinformation
Предметы:	Cropland mapping Agricultural census Multi-temporal Multi-modality Random forests
Online-ссылка:	http://www.sciencedirect.com/science/article/pii/S1569843223000158

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