  Hyperspectral Image Classification (HSC) is a challenging task due to the
high dimensionality and complex nature of Hyperspectral (HS) data. Traditional
Machine Learning approaches while effective, face challenges in real-world data
due to varying optimal feature sets, subjectivity in human-driven design,
biases, and limitations. Traditional approaches encounter the curse of
dimensionality, struggle with feature selection and extraction, lack spatial
information consideration, exhibit limited robustness to noise, face
scalability issues, and may not adapt well to complex data distributions. In
recent years, DL techniques have emerged as powerful tools for addressing these
challenges. This survey provides a comprehensive overview of the current trends
and future prospects in HSC, focusing on the advancements from DL models to the
emerging use of Transformers. We review the key concepts, methodologies, and
state-of-the-art approaches in DL for HSC. We explore the potential of
Transformer-based models in HSC, outlining their benefits and challenges. We
also delve into emerging trends in HSC, as well as thorough discussions on
Explainable AI and Interoperability concepts along with Diffusion Models (image
denoising, feature extraction, and image fusion). Additionally, we address
several open challenges and research questions pertinent to HSC. Comprehensive
experimental results have been undertaken using three HS datasets to verify the
efficacy of various conventional DL models and Transformers. Finally, we
outline future research directions and potential applications that can further
enhance the accuracy and efficiency of HSC. The Source code is available at
\url{https://github.com/mahmad00/Conventional-to-Transformer-for-Hyperspectral-Image-Classification-Survey-2024}.
