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.

翻译：高光谱图像分类（HSC）因高光谱数据的高维性与复杂性而面临重大挑战。传统机器学习方法虽已展现一定成效，但在实际应用中常遭遇显著障碍，包括最优特征集的可变性、人为设计的主观性、固有偏差以及方法学局限。具体而言，传统机器学习受困于维度灾难、特征选择与提取困难、空间信息考量不足、抗噪鲁棒性有限、可扩展性问题以及对复杂数据分布的适应能力欠缺。近年来，深度学习技术已成为应对这些挑战的有效方案。本综述全面梳理了HSC领域的当前趋势与未来前景，重点探讨从深度学习模型到日益普及的Transformer与Mamba模型架构的演进。我们系统回顾了HSC中深度学习的关键概念、方法论与前沿技术，深入探究了基于Transformer的模型与Mamba模型在高光谱分类中的潜力，详述其优势与挑战。文中进一步探讨了HSC的新兴趋势，包括可解释人工智能与互操作性概念的深度讨论，以及扩散模型在图像去噪、特征提取与图像融合中的应用。我们在三个高光谱数据集上进行了全面实验，验证了多种传统深度学习模型与Transformer架构的有效性。此外，本文指出了HSC领域中若干开放挑战及相关研究问题，最后展望了旨在提升HSC精度与效率的未来研究方向与潜在应用。