多源遥感卫星数据融合与机器学习在作物识别中的研究综述

傅群忠; 贾宇浩; 程斐斐; 徐春燕; 薛嘉卉; 张亚林

doi:10.11959/j.issn.2096-3750.WLW26022

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多源遥感卫星数据融合与机器学习在作物识别中的研究综述

更新时间：2026-07-03

- 多源遥感卫星数据融合与机器学习在作物识别中的研究综述
- Review on Multi-source Remote Sensing Satellite Data Fusion and Machine Learning in Crop Identification
- 物联网学报 2026年
- 作者机构：
  
  1.黄河科技学院工学部，河南郑州 450063
  2.黄河科技学院教务处，河南郑州，450063
  3.黄河科技学院医学部，河南郑州，450063
- 作者简介：
  
  傅群忠，智能科学与技术专业负责人，博士，副教授，研究方向：人工智能算法，机器人自主导航，操作系统，网络安全；
  2贾宇浩（2005年— ），男，黄河科技学院工学部在读，主要研究方向为计算机视觉，深度学习，图像识别。
  程斐斐（1988年— ），女，黄河科技学院工学部讲师、教学秘书，主要研究方向为用户行为分析挖掘、大数据系统开发与应用。
  徐春燕（1983年— ），女，黄河科技学院教务处副教授，主要研究方向为英语教学，英语语言学及应用语言学，教育管理。
  薛嘉卉（ 2005年— ），女，黄河科技学院工学部在读，主要研究方向为计算机视觉，深度学习，图像识别。
  张亚林（1985年— ），女，黄河科技学院医学部教授、科教中心副主任，主要研究方向为护理风险智能预警，创面
- 基金信息：
  
  国家级大学生创新训练项目(202511834018);中国高校产学研创新基金：新一代信息技术创新项目课题(2023IT043);河南省研究生教育改革与质量提升工程项目(YJS2025GZZ64);河南省高等教育教学改革研究与实践项目立项项目(2026SJGLX226);河南省科技厅软科学项目(252400410022)
- DOI：10.11959/j.issn.2096-3750.WLW26022
  中图分类号： TP391
- 收稿：2026-02-15，
  
  修回：2026-06-18，
  
  录用：2026-06-30，
- 稿件说明：
移动端阅览
傅群忠, 贾宇浩, 程斐斐, 等. 多源遥感卫星数据融合与机器学习在作物识别中的研究综述[J/OL]. 物联网学报, 2026.

FU Qunzhong, JIA Yuhao, CHENG Feifei, et al. Review on Multi-source Remote Sensing Satellite Data Fusion and Machine Learning in Crop Identification[J/OL]. Chinese Journal on Internet of Things, 2026.
傅群忠, 贾宇浩, 程斐斐, 等. 多源遥感卫星数据融合与机器学习在作物识别中的研究综述[J/OL]. 物联网学报, 2026. DOI： 10.11959/j.issn.2096-3750.WLW26022.

FU Qunzhong, JIA Yuhao, CHENG Feifei, et al. Review on Multi-source Remote Sensing Satellite Data Fusion and Machine Learning in Crop Identification[J/OL]. Chinese Journal on Internet of Things, 2026. DOI： 10.11959/j.issn.2096-3750.WLW26022.

摘要

在全球人口增长与环境变化背景下，为提高农作物识别效率与精度，研究系统梳理了光学、雷达和高光谱等多源遥感卫星数据的特点及适用场景，探讨了辐射校正、几何处理和分辨率统一等预处理方法。重点分析了时空融合、层级融合和抗干扰融合策略，以及支持向量机、随机森林等传统机器学习方法和卷积神经网络、循环神经网络、Transformer 等深度学习模型在作物识别中的应用。结果表明，多源数据融合与机器学习结合可显著提升识别精度，时空融合增强了作物生长曲线重建能力，决策级融合使分类精度达 92% 以上，深度学习模型在复杂场景中精度超过 93%。研究指出数据质量、模型泛化能力和计算资源等仍是主要挑战，未来需加强新型融合算法、深度融合技术和模型优化研究，以推动精准农业的数字化与智能化发展。

Abstract

Under the background of global population growth and environmental change

to improve the efficiency and accuracy of crop identification

this paper systematically reviews the characteristics and application scenarios of multi-source remote sensing satellite data

including optical

radar

and hyperspectral data

and discusses preprocessing methods such as radiometric correction

geometric processing

and resolution normalization. It focuses on spatiotemporal fusion

hierarchical fusion

and anti-interference fusion strategies

as well as the application of traditional machine learning methods (e.g.

support vector machines and random forests) and deep learning models (e.g.

convolutional neural networks

recurrent neural networks

and Transformers) in crop recognition. The results show that the combination of multi-source data fusion and machine learning can significantly improve identification accuracy. Spatiotemporal fusion enhances the ability to reconstruct crop growth curves

decision-level fusion achieves classification accuracy above 92%

and deep learning models exceed 93% accuracy in complex scenarios. This paper points out that data quality

model generalization

and computational resources remain major challenges

and future research should focus on novel fusion algorithms

deep integration technologies

and model optimization to promote the digitalization and intelligent development of precision agriculture.

关键词

Keywords

references

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