1. Introduction
2. Hyperspectral Data Representation
3. Learning Categories
4. Brief Overview of Conventional Methods
5. Advantages of Deep Learning
6. Convolutional Neural Networks for HSC
7. Deep Belief Network for HSC
8. Recurrent Neural Networks for HSC
9. Autoencoders for HSC
10. Limitations of Traditional Deep Learning
11. Transformers for HSC
12. Explainable AI and Interoperability
13. Diffusion Models for Hyperspectral Imaging
14. Open Challenges and Research Questions
15. Experimental Results and Discussion
16. Conclusions and Future Research Directions