UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review
In recent years, significant advances in unmanned aerial vehicle (UAV) technology and hyperspectral remote sensing have spurred rapid and innovative developments in UAV-based hyperspectral image (HSI) classification across a range of fields, including environmental monitoring, precision agriculture,...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10815625/ |
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| author | Zhen Zhang Lehao Huang Qingwang Wang Linhuan Jiang Yemao Qi Shunyuan Wang Tao Shen Bo-Hui Tang Yanfeng Gu |
| author_facet | Zhen Zhang Lehao Huang Qingwang Wang Linhuan Jiang Yemao Qi Shunyuan Wang Tao Shen Bo-Hui Tang Yanfeng Gu |
| author_sort | Zhen Zhang |
| collection | DOAJ |
| description | In recent years, significant advances in unmanned aerial vehicle (UAV) technology and hyperspectral remote sensing have spurred rapid and innovative developments in UAV-based hyperspectral image (HSI) classification across a range of fields, including environmental monitoring, precision agriculture, forest health assessment, and disaster management. Compared to spaceborne platforms, the spectra of ground objects observed by UAV platforms exhibit notable variations, presenting more pronounced challenges for accurate classification. This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. Although traditional methods demonstrate effectiveness in certain scenarios, their limitations become increasingly apparent when dealing with high-dimensional, nonlinear spectral data. In contrast, deep learning-based models excel at capturing intricate relationships between spectral and spatial features, significantly boosting classification accuracy and emerging as the dominant paradigm in the field. The WHU-Hi hyperspectral remote sensing dataset is utilized as a case study to elucidate the advantages and limitations of various deep learning methods through rigorous qualitative and quantitative comparisons. The potential of UAV hyperspectral image classification techniques in addressing high-dimensional data and complex scenarios is also thoroughly described. Furthermore, this article delves into cutting-edge research trends, such as lightweight model development, hyperspectral large models, multisource data fusion, and model interpretability, while also highlighting future trends for UAV hyperspectral remote sensing classification technology, particularly in real-time monitoring and intelligent applications. |
| format | Article |
| id | doaj-art-8ae3c1ce1d7847d3a328294e7296d6ea |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-8ae3c1ce1d7847d3a328294e7296d6ea2025-01-21T00:00:43ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183099312410.1109/JSTARS.2024.352231810815625UAV Hyperspectral Remote Sensing Image Classification: A Systematic ReviewZhen Zhang0https://orcid.org/0000-0002-2300-7112Lehao Huang1https://orcid.org/0009-0008-4239-2088Qingwang Wang2https://orcid.org/0000-0001-5820-5357Linhuan Jiang3https://orcid.org/0009-0006-7723-478XYemao Qi4Shunyuan Wang5Tao Shen6https://orcid.org/0000-0003-1273-7950Bo-Hui Tang7https://orcid.org/0000-0002-1918-5346Yanfeng Gu8https://orcid.org/0000-0003-1625-7989Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Information Engineering and Automation and Yunnan Key Laboratory of Computer Technologies Application, Kunming University of Science and Technology, Kunming, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Information Engineering and Automation and Yunnan Key Laboratory of Computer Technologies Application, Kunming University of Science and Technology, Kunming, ChinaFaculty of Information Engineering and Automation and Yunnan Key Laboratory of Computer Technologies Application, Kunming University of Science and Technology, Kunming, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaIn recent years, significant advances in unmanned aerial vehicle (UAV) technology and hyperspectral remote sensing have spurred rapid and innovative developments in UAV-based hyperspectral image (HSI) classification across a range of fields, including environmental monitoring, precision agriculture, forest health assessment, and disaster management. Compared to spaceborne platforms, the spectra of ground objects observed by UAV platforms exhibit notable variations, presenting more pronounced challenges for accurate classification. This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. Although traditional methods demonstrate effectiveness in certain scenarios, their limitations become increasingly apparent when dealing with high-dimensional, nonlinear spectral data. In contrast, deep learning-based models excel at capturing intricate relationships between spectral and spatial features, significantly boosting classification accuracy and emerging as the dominant paradigm in the field. The WHU-Hi hyperspectral remote sensing dataset is utilized as a case study to elucidate the advantages and limitations of various deep learning methods through rigorous qualitative and quantitative comparisons. The potential of UAV hyperspectral image classification techniques in addressing high-dimensional data and complex scenarios is also thoroughly described. Furthermore, this article delves into cutting-edge research trends, such as lightweight model development, hyperspectral large models, multisource data fusion, and model interpretability, while also highlighting future trends for UAV hyperspectral remote sensing classification technology, particularly in real-time monitoring and intelligent applications.https://ieeexplore.ieee.org/document/10815625/Deep learninghyperspectral applicationshyperspectral image classificationlarge remote sensing modelunmanned aerial vehicle (UAV) remote sensing |
| spellingShingle | Zhen Zhang Lehao Huang Qingwang Wang Linhuan Jiang Yemao Qi Shunyuan Wang Tao Shen Bo-Hui Tang Yanfeng Gu UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning hyperspectral applications hyperspectral image classification large remote sensing model unmanned aerial vehicle (UAV) remote sensing |
| title | UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review |
| title_full | UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review |
| title_fullStr | UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review |
| title_full_unstemmed | UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review |
| title_short | UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review |
| title_sort | uav hyperspectral remote sensing image classification a systematic review |
| topic | Deep learning hyperspectral applications hyperspectral image classification large remote sensing model unmanned aerial vehicle (UAV) remote sensing |
| url | https://ieeexplore.ieee.org/document/10815625/ |
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