首页|深度学习赋能的高光谱图像分类研究进展

深度学习赋能的高光谱图像分类研究进展

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
随着高光谱成像技术的发展,高光谱图像分类备受关注.在广泛调研的基础上,文章全面整理了基于深度学习的高光谱图像分类方法,主要涵盖深度网络、循环网络和自注意力网络.随后,深入讨论了几个具有代表性的方法,详细探讨了这些方法的优势和不足,旨在提供一个更清晰、全面的高光谱图像分类方法现状.文章对高光谱图像分类方法进行了全面的概述,并对各类方法进行了深入研究,分析了不同方法的定性和定量评估结果,对未来的发展进行了展望.梳理现有研究,不仅有助于推动高光谱遥感技术的进一步发展,还凸显了高光谱图像分类方法在航空航天等领域的独特优势,对于提高遥感数据的解译精度和实际应用价值具有重要意义.
Research Progress in Deep Learning-Enabled Hyperspectral Image Classification
With the development of hyperspectral imaging technology,hyperspectral image classification has become a re-search field of great interest.Based on extensive research,the hyperspectral image classification methods based on deep learning are organized comprehensively,mainly covering deep networks,recurrent networks and self-attention networks.Subsequently,several representative methods are discussed in depth,and the advantages and shortcomings of these meth-ods are explored in detail,aiming to provide a clearer and more comprehensive picture of the current status of hyperspec-tral image classification methods.A comprehensive overview of hyperspectral image classification methods is provided and an in-depth study of various methods is conducted,the qualitative and quantitative evaluation results of different methods are analyzed,and an outlook on the future development is provided.Sorting through existing research not only helps to promote the further development of hyperspectral remote sensing technology,but also highlights the unique ad-vantages of hyperspectral image classification methods in aerospace and other fields,which is of great significance to im-prove the interpretation accuracy and practical application value of remote sensing data.

hyperspectral image classificationdeep networksrecurrent networksself-attention networks

白林锋、陈增俊、周玲、张妍妍、路凯、张卫东

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河南科技学院信息工程学院,河南 新乡 453003

河南科技学院计算机应用研究所,河南 新乡 453003

许昌学院信息工程学院,河南 许昌 461000

高光谱图像分类 深度网络 循环网络 自注意力网络

河南省科技攻关项目河南省科技攻关项目河南省科技攻关项目河南省重点研发计划河南省自然科学青年基金河南省教师教育课程改革研究

2421022100752321022100182421022110592411112118002323004204282024-JSJYYB-099

2024

10.7682/j.issn.2097-1427.2024.05.003

海军航空大学学报
海军航空工程学院科研部

海军航空大学学报

CSTPCD
影响因子：0.279
ISSN：
年,卷(期)：2024.39(5)
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