A novel deep learning model for extracting arable land from high-resolution remote sensing images in hilly areas: a case study in the Sichuan Basin of Southwest China

A novel deep learning model for extracting arable land from high-resolution remote sensing images in hilly areas: a case study in the Sichuan Basin of Southwest China

Arable land is the fundamental guarantee of agricultural production, and accessing accurate arable land information is particularly crucial. A novel deep learning model named CNX-eMLP with ConvNeXt as the backbone and an enhanced Multilayer Perceptron (eMLP) as the decoder was proposed for arable la...

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Bibliographic Details
Main Authors: Yanxi Chen, Xingzhu Xiao, Yongle Zhang, Min Huang, Ziyi Tang, Hao Li
Format: Article
Language:English
Published: Taylor & Francis Group 2024-01-01
Series:Geocarto International
Subjects:
Deep learning
novel arable land extraction model
high-resolution remote sensing images
semantic segmentation
texture features
Online Access:https://www.tandfonline.com/doi/10.1080/10106049.2024.2400493
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https://www.tandfonline.com/doi/10.1080/10106049.2024.2400493

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