1. Introduction  
1.1. Limitations of Existing Surveys  
1.2. Significance of Accurate Classification  
1.3. Brief Introduction to Deep Learning  
1.4. Structure of the Survey  
2. Hyperspectral Data Representation  
3. Learning Categories  
4. Brief Overview of Conventional Methods  
5. Advantages of Deep Learning  
6. Convolutional Neural Networks for HSC  
6.1. Spectral CNN  
6.2. Spatial CNN  
6.3. Spectral-Spatial CNN  
6.4. Graph Convolutional Networks for HSC  
6.5. Future Research Directions  
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  
11.1. Benefits and Challenges of Transformers in HS Analysis  
11.2. Emerging Trends and Advancements  
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