@article{Lou2025, 
author = {Chen Lou and Mohammed A. A. Al-qaness and Dalal AL-Alimi and Abdelghani Dahou and Mohamed Abd Elaziz and Laith Abualigah and Ahmed A. Ewees},
title = {Land use/land cover (LULC) classification using hyperspectral images: a review},
year = {2025},
journal = {Geo-Spatial Information Science},
volume = {28},
number = {2},
pages = {345-386},
keywords = {Hyperspectral image, Land Use/Land Cover (LULC), deep learning, remote sensing},
url = {https://www.sciopen.com/article/10.1080/10095020.2024.2332638},
doi = {10.1080/10095020.2024.2332638},
abstract = {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.}
}