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...

書誌詳細
主要な著者:	Junshi Xia, Naoto Yokoya, Bruno Adriano, Keiichiro Kanemoto
フォーマット:	論文
言語:	English
出版事項:	Elsevier 2023-03-01
シリーズ:	International Journal of Applied Earth Observations and Geoinformation
主題:	Cropland mapping Agricultural census Multi-temporal Multi-modality Random forests
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S1569843223000158

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