Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of spectral bands, providing unparalleled levels of precision and accuracy in data analysis. Another technology gaining popularity in many industries is Autoencoders (AE). AE uses advanced deep learning algorithms for encoding and decoding data, leading to highly precise and efficient neural network-based models. Within the domain of HSI, AE emerges as a potent approach to tackle the essential hurdles associated with data analysis and feature extraction. Combining both HSI and AE (HSI – AE) can lead to a revolution in various industries, including but not limited to healthcare and environmental monitoring, because of more efficient analysis approaches and decision-making. AE can be used to discover hidden patterns and insights in large-scale datasets, allowing researchers to make more informed decisions based on much better predictions. Similarly, HSI can benefit from the scalability and flexibility AE offers, leading to faster and more efficient data processing. This article aims to provide a comprehensive review of the integration of HSI - AE, covering the history and background knowledge, motivation, and combined benefits of HSI and AE. It examines the applicability of HSI-AE in many use-case domains, such as classification, hyperspectral unmixing, and anomaly detection. It also provides a hyperparameter tuning and an in-depth survey of their use. The article emphasizes crucial areas for future exploration, such as conducting further research to enhance AE’s performance in HSI applications and devising novel algorithms to overcome the distinctive challenges presented by HSI data. Overall, the culmination of the HSI with AE can be seen as offering a promising solution for challenges like data analysis management and pattern recognition, enabling accurate and efficient decision-making across industries.

高光谱成像 (HSI) 是一种强大的工具，可以捕获和分析一系列光谱带，为数据分析提供无与伦比的精度和准确度。另一项在许多行业中越来越受欢迎的技术是自动编码器 (AE)。AE 使用先进的深度学习算法来编码和解码数据，从而产生高精度和高效的基于神经网络的模型。在 HSI 领域，AE 成为解决与数据分析和特征提取相关的基本障碍的有效方法。将 HSI 和 AE (HSI – AE) 结合起来，可以带来各个行业的革命，包括但不限于医疗保健和环境监测，因为分析方法和决策更加高效。AE 可用于发现大规模数据集中隐藏的模式和见解，使研究人员能够根据更好的预测做出更明智的决策。同样，HSI 可以受益于 AE 提供的可扩展性和灵活性，从而实现更快、更高效的数据处理。本文旨在对 HSI - AE 的集成进行全面回顾，涵盖 HSI 和 AE 的历史和背景知识、动机以及综合效益。它检查了 HSI-AE 在许多用例领域的适用性，例如分类、高光谱混合和异常检测。它还提供了超参数调整和对其使用的深入调查。本文强调了未来探索的关键领域，例如开展进一步研究以增强 AE 在 HSI 应用中的性能，以及设计新颖的算法来克服 HSI 数据带来的独特挑战。全面的，