Hyperspectral Imaging for Remote Sensing and Agriculture: A Comparative Study of Transformer-based Models

Hyperspectral Imaging (HSI) is a powerful tool in remote sensing and precision agriculture, providing insights into material characteristics. This review, “Advancements in Hyperspectral Imaging for Remote Sensing and Agriculture,” explores recent HSI developments. It covers HSI fundamentals, including sensor design and data analysis. It delves into remote sensing applications like land cover classification and environmental monitoring. In precision agriculture, HSI aids crop health assessment and yield prediction. The paper addresses HSI challenges like data preprocessing and dimensionality reduction. It discusses integrating machine learning and deep learning to improve HSI analysis. Emerging technologies, such as compact HSI sensors, Unmanned Aerial Vehicles (UAVs), and satellite systems, are highlighted. This paper presented a comparison study of Transformer based models for HSI in contrast to traditional convolutional neural networks challenges and their benefits in noise cancellation, Robust Spectral pixel extraction and group pixel awareness. In conclusion, this review offers a comprehensive look at HSI's role in sustainable agriculture and remote sensing. It emphasizes ongoing research and HSI's potential to shape these fields, aiding decision-making and resource management.