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Land use/land cover (LULC) classification using hyperspectral images: a review

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Publicado en:Geo-Spatial Information Science vol. 28, no. 2 (Apr 2025), p. 345
Autor principal: Chen, Lou
Otros Autores: Al-qaness, Mohammed A A, AL-Alimi, Dalal, Dahou, Abdelghani, Mohamed Abd Elaziz, Abualigah, Laith, Ewees, Ahmed A
Publicado:
Taylor & Francis Ltd.
Materias:
Land use
Image classification
Classification
Deep learning
Machine learning
Land cover
Hyperspectral imaging
Remote sensing
Environmental
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1009-5020
1993-5153
DOI:10.1080/10095020.2024.2332638
Fuente:Research Library