Hyperspectral imaging has shown tremendous growth over the past three decades. Hyperspectral imaging was evolved through remote sensing. Along, with the technological enhancements hyperspectral imaging has outgrown, conquering over other various application areas. In addition to it, data enriched data cubes with abundant spectral and spatial information works as perk for capturing, analyzing, reviewing, and interpreting results from data. This review concentrates on emerging application areas of hyperspectral imaging. Emerging application areas are selected in ways where there is a vast scope for future enhancements by exploiting cutting edge technology, that is, deep learning. Applications of hyperspectral imaging techniques in some selected areas (remote sensing, document forgery, history and archaeology conservation, surveillance and security, machine vision for fruit quality inspection, medical imaging) are focused. The review pivots around the publicly available datasets and features used domain wise. This review can act as a baseline for deep learning and machine vision experts, historical geographers, and scholars by providing them a view of how hyperspectral imaging is implemented in multiple domains along with future research prospects.

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通过深度学习深入了解现代高光谱图像应用

在过去的三年中，高光谱成像显示出巨大的增长。高光谱成像是通过遥感发展起来的。随着技术的进步，高光谱成像已经过时，征服了其他各种应用领域。除此之外，数据丰富的数据立方体具有丰富的光谱和空间信息，可作为捕获、分析、审查和解释数据结果的特权。本综述重点关注高光谱成像的新兴应用领域。新兴应用领域的选择方式是通过利用尖端技术（即深度学习）在未来具有广阔的增强空间。高光谱成像技术在某些选定领域的应用（遥感、文件伪造、历史和考古保护、监视和安全、用于水果质量检测、医学成像的机器视觉）是重点。审查围绕公开可用的数据集和领域明智使用的功能进行。这篇综述可以作为深度学习和机器视觉专家、历史地理学家和学者的基线，让他们了解如何在多个领域实施高光谱成像以及未来的研究前景。