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

• Published in: Geo-spatial information science Vol. 28; no. 2; pp. 345 - 386
• Main Authors: Lou, Chen, Al-qaness, Mohammed A. A., AL-Alimi, Dalal, Dahou, Abdelghani, Abd Elaziz, Mohamed, Abualigah, Laith, Ewees, Ahmed A.
• Format: Journal Article
• Language: English
• Published: Wuhan Taylor & Francis 04.03.2025; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Classification; Deep learning; Hyperspectral image; Hyperspectral imaging; Image classification; Land cover; Land use; Land Use/Land Cover (LULC); Machine learning; Remote sensing
• ISSN: 1009-5020; 1993-5153
• DOI: 10.1080/10095020.2024.2332638

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.