Exploring Deep Learning Models for Hyperspectral Image Classification

Hyperspectral image (HSI) classification plays a pivotal role in remote sensing applications, enabling detailed analysis of Earth's resources through the integration of imaging and spectroscopy. This multi-dimensional imaging technique captures intricate spectral information, which is essential for urban monitoring, vegetation classification, land cover mapping, and resource management. However, the complexity and high dimensionality of HSIs pose significant challenges for accurate and efficient classification. This research addresses the critical issue of extracting comprehensive spectral and spatial information from HSIs by evaluating and comparing various classification methods, including traditional approaches and advanced Deep Learning models. The study highlights the limitations of conventional techniques and demonstrates the superior performance of Deep Learning algorithms in terms of precision, efficiency, and reliability. The outcomes of this work provide valuable insights into the selection and optimization of HSI classification methods, contributing to more effective utilization of hyperspectral data for environmental and resource management applications.