Hyperspectral Image Classification Using Deep Learning: A Recent Overview

Hyperspectral imaging (HSI) plays a pivotal role in numerous fields, offering rich and detailed information across a wide range of wavelengths. This article is about HSI classification. It talks about the problems that come up when you try to analyze high-dimensional hyperspectral data and looks at how well convolutional neural networks (CNNs) work in this situation. We begin by defining hyperspectral image classification and outlining its significance in various applications, ranging from agriculture to remote sensing. Next, we discuss the issues encountered in HSI analysis and examine the standard approaches employed for classification tasks. Our focus then shifts to CNN-based HSI classification, where we conduct experiments using three distinct CNN architectures. Through rigorous experimentation and analysis, we demonstrate the potential of CNNs in accurately classifying hyperspectral data, with the 3DCNN model emerging as the most promising, achieving a remarkable accuracy of 97%. These findings underscore the effectiveness of CNNs in leveraging spatial and spectral for HSI classification. Our study makes a valuable contribution to the progress of hyperspectral image analysis, paving the way for innovative solutions that unlock valuable insights from hyperspectral data across various domains.