Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.

翻译：在许多现实生活中应用中广泛使用了超光谱成像(HSI),因为它受益于每个像素中所包含的详细的光谱信息。值得注意的是,复杂的特征,即所捕获的光谱信息与高光谱数据的相应对象之间的非线性关系,对传统方法提出了准确的分类挑战。在过去几年中,深学(DL)被证实为一个强大的特征提取器,有效地解决一些计算机愿景任务中出现的非线性问题。这促使为HSI分类(HSIC)部署DL,该分类显示了良好的绩效。这次调查对HSIC的DL进行了系统化的概览,并比较了该主题的最新战略的比较。我们首先将概括传统机器学习对传统方法的挑战,然后我们将将DL的优势转化为解决这些问题的强大特征提取器。这项调查将最新水平的DL框架细分为光谱性能、空间性能和综合空间光谱特征,以便系统地分析成就(未来HSIC的研究方向,作为该主题的最新战略),我们将为HSIC的大规模性能定义提供一个挑战性框架,同时我们还要考虑为HSIC提供高额的将来的训练指标。