红外与毫米波学报2017，Vol.36Issue(2)：173-185,13.DOI:10.11972/j.issn.1001-9014.2017.02.009
高光谱遥感图像非线性解混研究综述
Review of nonlinear unmixing for hyperspectral remote sensing imagery
摘要
Abstract
The development of non-linear spectral unmixing methods in recent years is introduced.There are mainly two types of models.One is the close-mixing model of mineral sand area and the other is multi-level mixing model of vegetation coverage area.The data-driven nonlinear spectral unmixing algorithms such as kernel methods and manifold learning are presented.Both advantages and disadvantages of these models and algorithms are summarized and the future research trends are analyzed.关键词
高光谱遥感/混合像元/非线性光谱解混/Hapke模型/双线性混合模型/核方法/流形学习Key words
hyperspectral remote sensing/mixed pixel/nonlinear spectral unmixing/Hapke model/bilinear mixture model/kernel method/manifold learning分类
信息技术与安全科学引用本文复制引用
杨斌,王斌..高光谱遥感图像非线性解混研究综述[J].红外与毫米波学报,2017,36(2):173-185,13.基金项目
国家自然科学基金(61572133),北京师范大学地表过程与资源生态国家重点实验室开放基金(2015-KF-01)Supported by National Natural Science Foundation of China (61572133),Research Fund for the State Key Laboratory of Earth Surface Processes and Resource Ecology (2015-KF-01) （61572133）
