School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar 751024, India.
KIET Group of Institutions, Delhi-NCR, Ghaziabad-201206, India.
Comput Intell Neurosci. 2022 Apr 28;2022:3854635. doi: 10.1155/2022/3854635. eCollection 2022.
Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies' contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.
近期的成像科学和技术发现已经考虑了高光谱图像和遥感。目前的智能技术，如支持向量机、稀疏表示、主动学习、极限学习机、迁移学习和深度学习，通常基于机器的学习。这些技术通过其精度和保真度丰富了对这种三维、多波段和高分辨率图像的处理。本文对基于高光谱图像的土地覆盖分类的机器依赖技术的贡献和深度学习进行了广泛的调查。本研究的目的有三。首先，在阅读了大量 Web of Science (WoS)、Scopus、SCI 和 SCIE 索引和 SCIE 相关文章后，我们提供了一种全新的、完全系统的综述工作方法，有助于启发发现研究空白和提出嵌入式问题。其次，我们强调了用于识别高光谱图像的机器学习 (ML) 方法的最新进展，对相关文献进行了简要的、有组织的概述和全面评估。最后，我们得出结论，以帮助研究人员扩大对机器学习和高光谱图像之间关系的理解，从而为未来的研究提供帮助。
