Hyperspectral image (HSI) classification is a significant topic of discussion in real-world applications. The prevalence of these applications stems from the precise spectral information offered by each pixelś data in hyperspectral imaging (HS). Classical machine learning (ML) methods face challenges in precise object classification with HSI data complexity. The intrinsic non-linear relationship between spectral information and materials complicates the task. Deep learning (DL) has proven to be a robust feature extractor in computer vision, effectively addressing nonlinear challenges. This validation drives its integration into HSI classification, which proves to be highly effective. This review compares DL approaches to HSI classification, highlighting its superiority over classical ML algorithms. Subsequently, a framework is constructed to analyze current advances in DL-based HSI classification, categorizing studies based on a network using only spectral features, spatial features, or both spectral–spatial features. Moreover, we have explained a few recent advanced DL models. Additionally, the study acknowledges that DL demands a substantial number of labeled training instances. However, obtaining such a large dataset for the HSI classification framework proves to be time and cost-intensive. So, we also explain the DL methodologies, which work well with the limited training data availability. Consequently, the survey introduces techniques aimed at enhancing the generalization performance of DL procedures, offering guidance for the future.

中文翻译：

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高光谱图像分类的深度学习：一项调查


高光谱图像（HSI）分类是现实应用中讨论的一个重要话题。这些应用的流行源于高光谱成像 (HS) 中每个像素数据提供的精确光谱信息。经典机器学习 (ML) 方法面临着 HSI 数据复杂性的精确对象分类挑战。光谱信息和材料之间固有的非线性关系使任务变得复杂。深度学习 (DL) 已被证明是计算机视觉中强大的特征提取器，可以有效解决非线性挑战。这一验证推动了其与 HSI 分类的整合，事实证明这是非常有效的。这篇综述将 DL 方法与 HSI 分类进行了比较，强调了它相对于经典 ML 算法的优越性。随后，构建了一个框架来分析基于深度学习的 HSI 分类的当前进展，基于仅使用光谱特征、空间特征或两者的光谱特征的网络对研究进行分类。此外，我们还解释了一些最近的高级深度学习模型。此外，该研究承认深度学习需要大量标记的训练实例。然而，为 HSI 分类框架获取如此大的数据集被证明是时间和成本密集型的​​。因此，我们还解释了深度学习方法，该方法在训练数据可用性有限的情况下效果很好。因此，该调查引入了旨在增强深度学习程序泛化性能的技术，为未来提供指导。