Crop classification in South Korea for multitemporal PlanetScope imagery using SFC-DenseNet-AM

In this manuscript, a new methodology based on a deep learning model using a Siamese network and attention module was proposed to classify crop cultivation areas, such as onion and garlic, from multitemporal PlanetScope images in South Korea. To consider the seasonal characteristics of crops in the...

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מידע ביבליוגרפי

Principais autores:	Seonkyeong Seong, Anjin Chang, Junsang Mo, Sangil Na, Hoyong Ahn, Jaehong Oh, Jaewan Choi
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Elsevier 2024-02-01
סדרה:	International Journal of Applied Earth Observations and Geoinformation
נושאים:	Attention module Cultivated area Deep learning SFC-DenseNet-AM multitemporal PlanetScope imagery
גישה מקוונת:	http://www.sciencedirect.com/science/article/pii/S1569843223004430
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