低空间分辨率和物质异质性等因素造成的图像混合像元问题,使像元级的数据处理和应用难以满足实际需求。光谱解混提取亚像元尺度上的端元和丰度信息,为现实应用的数据精细化定量分析提供技术支撑。本文介绍了近些年光谱解混理论方法和应用的相关研究进展,包括线性与非线性混合模型作用,以及几何、正则优化和统计机器学习原理框架下的方法研究成果。此外,分析了光谱解混对分类等其他技术性能的改善作用以及该技术解决从遥感到医学等室内级应用问题的理论和实际价值。最后,总结了光谱解混技术与应用研究中的不足和构建二者协同发展的必要性。

Factors such as low spatial resolution and material heterogeneity result in the issue of mixed pixels in images, which makes pixel-level data processing and applications unable to meet the practical requirements. Spectral unmixing extracts information of endmembers and abundances at the sub-pixel level and offers technical support for fine quantitative analysis of data in real applications. This paper introduces research advances of spectral unmixing theories, methods, and applications in recent years. Technical research results include linear and nonlinear mixture models, and methods under the principle frameworks of geometry, regularized optimization, and statistical machine learning. Moreover, improvements provided by spectral unmixing for other techniques such as classification, and theoretical and practical values of spectral unmixing in handling problems from remote sensing to indoor applications such as medical science are analyzed. Finally, drawbacks in spectral unmixing technical and application researches and the necessity of their synergistic development are summarized.