综述高光谱解混技术，助力遥感表面物质识别与分布分析
Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the art
- 系统梳理高光谱解混主流方法及其性能对比
- 涵盖常用公开数据集及验证基准
- 指出现有挑战并提出未来研究方向
本文详细回顾了用于地球及其他固态天体大范围遥感图像的数据分析方法。重点聚焦于从高光谱图像中推断地表覆盖物质、估算其丰度及空间分布的问题。文中总结并比较了最成功且相关的高光谱解混方法，同时分析了最新技术进展。系统梳理了该领域广泛使用的公开数据集，这些数据集在方法测试与验证中起关键作用。最后，指出当前未解决的核心问题，并为未来研究提供具体建议。
原文摘要 · Abstract (English)
This work concerns a detailed review of data analysis methods used for remotely sensed images of large areas of the Earth and of other solid astronomical objects. In detail, it focuses on the problem of inferring the materials that cover the surfaces captured by hyper-spectral images and estimating their abundances and spatial distributions within the region. The most successful and relevant hyper-spectral unmixing methods are reported as well as compared, as an addition to analysing the most recent methodologies. The most important public data-sets in this setting, which are vastly used in the testing and validation of the former, are also systematically explored. Finally, open problems are spotlighted and concrete recommendations for future research are provided.
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