Land use/land cover (LULC) classification using hyperspectral images: a review
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| Xuất bản năm: | Geo-Spatial Information Science vol. 28, no. 2 (Apr 2025), p. 345 |
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| Tác giả chính: | |
| Tác giả khác: | , , , , , |
| Được phát hành: |
Taylor & Francis Ltd.
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| Những chủ đề: | |
| Truy cập trực tuyến: | Citation/Abstract Full Text - PDF |
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| 001 | 3224790717 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1009-5020 | ||
| 022 | |a 1993-5153 | ||
| 024 | 7 | |a 10.1080/10095020.2024.2332638 |2 doi | |
| 035 | |a 3224790717 | ||
| 045 | 2 | |b d20250401 |b d20250430 | |
| 100 | 1 | |a Chen, Lou |u College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China | |
| 245 | 1 | |a Land use/land cover (LULC) classification using hyperspectral images: a review | |
| 260 | |b Taylor & Francis Ltd. |c Apr 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In the rapidly evolving realm of remote sensing technology, the classification of Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent limitations in hyperspectral imaging, enhancing the accuracy and efficiency of HSI classification remains a critical and much-debated issue. This review study focuses on a key application area in HSI classification: Land Use/Land Cover (LULC). Our study unfolds in fourfold approaches. First, we present a systematic review of LULC hyperspectral image classification, delving into its background and key challenges. Second, we compile and analyze a number of datasets specific to LULC hyperspectral classification, offering a valuable resource. Third, we explore traditional machine learning models and cutting-edge methods in this field, with a particular focus on deep learning, and spectral decomposition techniques. Finally, we comprehensively analyze future developmental trajectories in HSI classification, pinpointing potential research challenges. This review aspires to be a cornerstone resource, enlightening researchers about the current landscape and future prospects of hyperspectral image classification. | |
| 653 | |a Land use | ||
| 653 | |a Image classification | ||
| 653 | |a Classification | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Land cover | ||
| 653 | |a Hyperspectral imaging | ||
| 653 | |a Remote sensing | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Al-qaness, Mohammed A A |u College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China; Zhejiang Optoelectronics Research Institute, Jinhua, China | |
| 700 | 1 | |a AL-Alimi, Dalal |u School of Computer Science, China University of Geosciences, Wuhan, China | |
| 700 | 1 | |a Dahou, Abdelghani |u Faculty of Computer Sciences and Mathematics, Ahmed Draia University, Adrar, Algeria | |
| 700 | 1 | |a Mohamed Abd Elaziz |u Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt; Faculty of Computer Science & Engineering, Galala University, Suze, Egypt; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates | |
| 700 | 1 | |a Abualigah, Laith |u Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Jordan | |
| 700 | 1 | |a Ewees, Ahmed A |u Department of Computer, Damietta University, Damietta, Egypt | |
| 773 | 0 | |t Geo-Spatial Information Science |g vol. 28, no. 2 (Apr 2025), p. 345 | |
| 786 | 0 | |d ProQuest |t Research Library | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3224790717/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3224790717/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |