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首页> 外文会议>Conference on Image and Signal Processing for Remote Sensing VIII, Sep 24-27, 2002, Agia Pelagia, Crete, Greece >Application of convex cone analysis to hyper-spectral and multi-spectral scenes
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Application of convex cone analysis to hyper-spectral and multi-spectral scenes

机译：凸锥分析在高光谱和多光谱场景中的应用

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摘要

A new end-member analysis method based on convex cones has been developed. The method finds extreme points in a convex set. Unlike convex methods that rely on a simplex, the number of end-members is not restricted by the number of spectral channels. The algorithm simultaneously finds fractional abundance maps. The fractional abundances are the fractions of the total spectrally integrated radiance of a pixel that are contributed by the end-members. A physical model of the hyper-spectral or multi-spectral scene is obtained by combining subsets of the end-members into bundles of spectra for each scene material. The bundle spectra represent the spectral variability of the material in the scene induced by illumination, shadowing, weathering and other environmental effects. The method offers advantages in multi-spectral data sets where the limited number of channels impairs material un-mixing by standard techniques. The method can also be applied to compress hyper-spectral data. The fractional abundance matrices are sparse and offer an additional compression capability over standard matrix factorization techniques. A description of the method and applications to real and synthetic hyper-spectral and multi-spectral data sets will be presented.
机译：提出了一种新的基于凸锥的端构件分析方法。该方法在凸集中找到极点。与依赖于单纯形的凸方法不同，末端成员的数量不受频谱通道数量的限制。该算法同时找到分数丰度图。分数丰度是末端成员贡献的像素的总光谱积分辐射度的分数。高光谱或多光谱场景的物理模型是通过将端成员的子集组合到每种场景材料的光谱束中而获得的。束光谱表示由照明，阴影，风化和其他环境影响引起的场景中材料的光谱可变性。该方法在多光谱数据集中提供了优势，在这些数据集中，有限数量的通道会损害通过标准技术进行的材料混合。该方法还可以应用于压缩高光谱数据。分数丰度矩阵稀疏，并且比标准矩阵分解技术具有更多的压缩功能。将描述该方法及其对真实和合成的高光谱和多光谱数据集的应用。

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