An Overview of Hyperspectral Image Classification by Data-driven Deep Learning

Xiaochuan Yu, Mary B. Ozdemir, M. K. Joshie
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Abstract

Hyperspectral imaging (HSI) in remote sensing is gaining significant attention due to its complexity, posing challenges for conventional machine learning in achieving accurate classification. The inherent nonlinear relationship between captured spectral information and materials further complicates hyperspectral imaging. Deep learning has emerged as an effective tool for feature extraction, finding widespread applications in image processing tasks. Motivated by its success, this survey integrates deep learning into hyperspectral imaging (HSI) classification, demonstrating commendable performance. The paper systematically reviews existing literature, providing a comparative analysis of strategies. Primary challenges in HSI classification for traditional methods are outlined, emphasizing the advantages of deep learning. Our framework categorizes works into three types: spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks, offering a comprehensive review of recent achievements and diverse approaches. Considering limited training samples in remote sensing and substantial data requirements for deep networks, strategies to enhance classification performance are presented, offering valuable insights for future studies. Experiments apply representative deep learning-based classification methods to real HSIs, providing practical validation. The survey contributes to understanding the current landscape in deep learning-based HSI classification and lays a foundation for future research in this evolving field.
数据驱动深度学习的高光谱图像分类概述
遥感中的高光谱成像(HSI)因其复杂性而备受关注,它对传统机器学习实现精确分类提出了挑战。捕捉到的光谱信息与材料之间固有的非线性关系使高光谱成像更加复杂。深度学习已成为特征提取的有效工具,在图像处理任务中得到广泛应用。受其成功的激励,本研究将深度学习整合到高光谱成像(HSI)分类中,并展示了值得称道的性能。本文系统回顾了现有文献,对各种策略进行了比较分析。本文概述了传统方法在高光谱成像分类中面临的主要挑战,强调了深度学习的优势。我们的框架将作品分为三类:光谱特征网络、空间特征网络和光谱-空间特征网络,全面回顾了最新成果和各种方法。考虑到遥感中有限的训练样本和深度网络的大量数据要求,介绍了提高分类性能的策略,为未来的研究提供了宝贵的见解。实验将基于深度学习的代表性分类方法应用于真实的恒星图像,提供了实际验证。这项调查有助于了解基于深度学习的人机交互分类的现状,并为这一不断发展的领域的未来研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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