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从丘陵地区高分辨率遥感图像中提取耕地的新型深度学习模型：中国西南四川盆地的案例研究

IF 3.3 4区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Geocarto International Pub Date : 2024-09-06 DOI:10.1080/10106049.2024.2400493
Yanxi Chen, Xingzhu Xiao, Yongle Zhang, Min Huang, Ziyi Tang, Hao Li
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引用次数: 0

摘要

耕地是农业生产的根本保障，获取准确的耕地信息尤为重要。一种名为 CNX-eMLP 的新型深度学习模型与 ConvNeXt...
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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...
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来源期刊
Geocarto International
Geocarto International ENVIRONMENTAL SCIENCES-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
6.30
自引率
13.20%
发文量
407
审稿时长
>12 weeks
期刊介绍： Geocarto International is a professional academic journal serving the world-wide scientific and user community in the fields of remote sensing, GIS, geoscience and environmental sciences. The journal is designed: to promote multidisciplinary research in and application of remote sensing and GIS in geosciences and environmental sciences; to enhance international exchange of information on new developments and applications in the field of remote sensing and GIS and related disciplines; to foster interest in and understanding of science and applications on remote sensing and GIS technologies; and to encourage the publication of timely papers and research results on remote sensing and GIS applications in geosciences and environmental sciences from the world-wide science community. The journal welcomes contributions on the following: precise, illustrated papers on new developments, technologies and applications of remote sensing; research results in remote sensing, GISciences and related disciplines; Reports on new and innovative applications and projects in these areas; and assessment and evaluation of new remote sensing and GIS equipment, software and hardware.
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