Interpretable Hyperspectral Artificial Intelligence: When nonconvex modeling meets hyperspectral remote sensing
Interpretable Hyperspectral Artificial Intelligence: When nonconvex modeling meets hyperspectral remote sensing
复制标题
可解释的高光谱人工智能：当非凸建模满足高光谱遥感时
DOI：
10.1109/mgrs.2021.3064051
复制
发表时间：
2021-06-01
影响因子：
14.6
通讯作者：
Zhu, Xiaoxiang
中科院分区：
文献类型：
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作者：
Hong, Danfeng;He, Wei;Zhu, Xiaoxiang
高光谱成像（HS），也称为图像光谱法，是地球科学和遥感（RS）中的一项里程碑式的技术。在过去的十年中，主要由经验丰富的专家做出了巨大的努力来处理和分析这些HS产品。然而，随着数据量的不断增长，大量的人力和物力成本对减轻手工劳动负担和提高效率提出了新的挑战。因此，迫切需要为各种HS RS应用开发更智能和自动化的方法。具有凸优化的机器学习（ML）工具已经成功地承担了许多人工智能（AI）相关应用的任务;然而，由于HS成像过程中各种光谱变化的影响以及高维HS信号的复杂性和冗余性，它们处理复杂实际问题的能力仍然有限，特别是对于HS数据。与凸模型相比，非凸模型能够表征更复杂的真实的场景，并在技术和理论上提供模型可解释性，已被证明是一种可行的解决方案，可以缩小具有挑战性的HS视觉任务与当前先进的智能数据处理模型之间的差距。
Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these HS products, mainly by seasoned experts. However, with an ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges for reducing the burden of manual labor and improving efficiency. For this reason, it is urgent that more intelligent and automatic approaches for various HS RS applications be developed. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications; however, their ability to handle complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher-dimensional HS signals. Compared to convex models, nonconvex modeling, which is capable of characterizing more complex real scenes and providing model interpretability technically and theoretically, has proven to be a feasible solution that reduces the gap between challenging HS vision tasks and currently advanced intelligent data processing models.