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

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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Bibliographic Details
Main Authors: Seonkyeong Seong, Anjin Chang, Junsang Mo, Sangil Na, Hoyong Ahn, Jaehong Oh, Jaewan Choi
Format: Article
Language:English
Published: Elsevier 2024-02-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Attention module
Cultivated area
Deep learning
SFC-DenseNet-AM
multitemporal PlanetScope imagery
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843223004430

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