A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

• URL: http://arxiv.org/abs/2404.14955v4
• Date: Thu, 14 Nov 2024 07:37:28 GMT
• Title: A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models
• Authors: Muhammad Ahmad, Salvatore Distifano, Adil Mehmood Khan, Manuel Mazzara, Chenyu Li, Hao Li, Jagannath Aryal, Yao Ding, Gemine Vivone, Danfeng Hong,
• Abstract summary: Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of HS data. Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC.
• Score: 25.18873183963132
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• Abstract: Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. Comprehensive experimental results were conducted on three HS datasets to substantiate the efficacy of various conventional DL models and Transformers. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and efficiency of HSC.

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