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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis
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Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis

机译：端成员束：将端成员变异性纳入光谱混合物分析的新方法

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Accuracy of vegetation cover fractions, computed with spectral mixture analysis, may be compromised by variation in canopy structure and biochemistry when a single endmember represents top-of-canopy reflectance. In this article, endmember variability is incorporated into mixture analysis by representing each endmember by a set or bundle of spectra, each of which could reasonably be the reflectance of an instance of the endmember. Endmember bundles are constructed from the data itself by an extension to a previously described method of manually deriving endmembers from remotely sensed data. Applied to remotely sensed images, bundle unmixing produces maximum and minimum fraction images bounding the correct cover fractions and specifying error due to endmember variability. In this article, endmember bundles and bounding fraction images were created for an airborne visible/infrared imaging spectrometer (AVIRIS) subscene simulated with a canopy radiative transfer/geometric-optical model. Variation in endmember reflectance was achieved using ranges of parameter values including leaf area index (LAI) and tissue optical properties observed In a North Texas savanna. The subscene's spatial pattern was based on a 1992 Landsat Thematic Mapper image of the study region. Bounding fraction images bracketed the cover fractions of the simulated data for 98% of the pixels for soil, 97% for senescent grass and 93% for trees. Averages of bounding images estimated fractional coverage used in the simulation with an average error of /spl les/0.05, a significant improvement over previous methods with important implications for regional-scale research on vegetation extent and dynamics.
机译：当单个末端代表顶冠反射率时，通过光谱混合分析计算出的植被覆盖率的准确性可能会因冠层结构和生物化学的变化而受到影响。在本文中，通过用一组光谱或一束光谱表示每个末端成员，将末端成员的可变性结合到混合物分析中，每个光谱都可以合理地视为末端成员实例的反射率。通过对先前描述的从遥感数据手动导出端成员的方法的扩展，可以从数据本身构造端成员束。应用于遥感图像时，束分解将产生最大和最小分数图像，这些图像将边界覆盖正确的分数并指定由于端成员可变性而引起的错误。在本文中，使用机盖辐射传输/几何光学模型模拟了机载可见/红外成像光谱仪（AVIRIS）子场，创建了端构件束和边界分数图像。使用在北德克萨斯大草原中观察到的参数值范围，包括叶面积指数（LAI）和组织光学特性，可以实现端构件反射率的变化。该子场景的空间模式基于研究区域的1992年Landsat专题测绘仪图像。边界分数图像将模拟数据的覆盖分数括起来，其中98％的像素是土壤，97％的是衰老草，93％的是树木。边界图像的平均值估计了模拟中使用的分数覆盖率，平均误差为/ spl les / 0.05，比以前的方法有了显着改进，对植被范围和动态的区域规模研究具有重要意义。

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