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.

中文翻译：

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可解释的高光谱人工智能：当非凸建模遇到高光谱遥感

高光谱 (HS) 成像，也称为图像光谱法，是地球科学和遥感 (RS) 中的一项里程碑式技术。在过去的十年中，主要由经验丰富的专家在处理和分析这些 HS 产品方面付出了巨大的努力。然而，随着数据量的不断增长，大量的人力物力成本对减轻体力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) 工具已成功承担了众多人工智能 (AI) 相关应用的任务；然而，他们处理复杂实际问题的能力仍然有限，特别是对于 HS 数据，由于 HS 成像过程中各种光谱变异的影响以及高维 HS 信号的复杂性和冗余性。与凸模型相比，非凸模型能够表征更复杂的真实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。