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标记样本有限地区的作物分类解决方案：深度学习和迁移学习

IF 6 2区 地球科学 Q1 GEOGRAPHY, PHYSICAL
GIScience & Remote Sensing Pub Date : 2024-08-05 DOI:10.1080/15481603.2024.2387393
Hengbin Wang, Yu Yao, Zijing Ye, Wanqiu Chang, Junyi Liu, Yuanyuan Zhao, Shaoming Li, Zhe Liu, Xiaodong Zhang
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引用次数: 0

摘要

可靠的分类结果对于指导农业生产、预测作物产量和确保粮食安全至关重要。生成可靠的分类结果相对简单 ...
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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Solution for crop classification in regions with limited labeled samples: deep learning and transfer learning
Reliable classification results are crucial for guiding agricultural production, forecasting crop yield, and ensuring food security. Generating reliable classification results is relatively simple ...
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来源期刊
GIScience & Remote Sensing
GIScience & Remote Sensing 地学-遥感
CiteScore
11.20
自引率
9.00%
发文量
84
审稿时长
6 months
期刊介绍： GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.
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