Traditional to Transformers: A Survey on Current Trends and Future Prospects for Hyperspectral Image Classification

Hyperspectral image classification is a challenging task due to the high dimensionality and complex nature of hyperspectral data. In recent years, deep learning techniques have emerged as powerful tools for addressing these challenges. This survey provides a comprehensive overview of the current trends and future prospects in hyperspectral image classification, focusing on the advancements from deep learning models to the emerging use of transformers. We review the key concepts, methodologies, and state-of-the-art approaches in deep learning for hyperspectral image classification. Additionally, we discuss the potential of transformer-based models in this field and highlight the advantages and challenges associated with these approaches. Comprehensive experimental results have been undertaken using three Hyperspectral datasets to verify the efficacy of various conventional deep-learning models and Transformers. Finally, we outline future research directions and potential applications that can further enhance the accuracy and efficiency of hyperspectral image classification. The Source code is available at https://github.com/mahmad00/Conventional-to-Transformer-for-Hyperspectral-Image-Classification-Survey-2024.