Content area
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.
Details
; Al-qaness, Mohammed A A 2
; AL-Alimi, Dalal 3
; Dahou, Abdelghani 4
; Mohamed Abd Elaziz 5
; Abualigah, Laith 6
; Ewees, Ahmed A 7
1 College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China
2 College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China; Zhejiang Optoelectronics Research Institute, Jinhua, China
3 School of Computer Science, China University of Geosciences, Wuhan, China
4 Faculty of Computer Sciences and Mathematics, Ahmed Draia University, Adrar, Algeria
5 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
6 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Jordan
7 Department of Computer, Damietta University, Damietta, Egypt