DataSheet1_Joint Characterization of the Cryospheric Spectral Feature Space.docx

Multispectral and hyperspectral feature spaces are useful for a variety of remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In many of these applications, models are used to project the higher dimensional continuum of reflectances (or radiances) onto lower dimensional mappings of the image target’s physical properties or categorical composition. In such cases, characterization of the feature space dimensionality, geometry and topology can provide fundamental guidance for effective model design. Utility of this characterization, however, hinges on identification of appropriate basis vectors for the feature space. The objective of this study is to compare and contrast two fundamentally different approaches for identifying feature space basis vectors via dimensionality reduction. In so doing, we illustrate how these two approaches can be combined to render a joint characterization that reveals spectral properties not apparent using either approach alone. We use a diverse collection of AVIRIS-NG reflectance spectra of ice and snow to illustrate the utility of the joint characterization to facilitate both modeling and classification of snow and ice reflectance. Joint characterization is also shown to assist with interpretation of physical properties inferred from the spectra. Spectral feature spaces combining principal components (PCs) and t-distributed Stochastic Neighbor Embeddings (t-SNEs) provide both physically interpretable dimensions representing the global structure of cryospheric reflectance properties as well as local manifold structures revealing clustering not resolved within the global continuum. The joint characterization reveals distinct continua for snow-firn gradients on different parts of the Greenland Ice Sheet and multiple clusters of ice reflectance properties common to both glacier and sea ice in different locations. The clustering revealed in the t-SNE feature spaces, and extended to the joint characterization, distinguishes subtle differences in spectral curvature specific to different spatial locations within the snow accumulation zone, as well as BRDF effects related to view geometry. The ability of the PC + t-SNE joint characterization to produce a physically interpretable spectral feature space revealing global topology while preserving local manifold structures for cryospheric hyperspectra suggests that this type of characterization might be extended to the much higher dimensional hyperspectral feature space of all terrestrial land cover.

多光谱和超光谱特征空间对于多种遥感应用具有重要意义，这些应用范围从光谱混合建模到离散主题分类。在这些应用中，模型通常被用于将反射率（或辐射度）的高维连续体投影到图像目标的物理属性或类别组成的低维映射。在这种情况下，对特征空间维度、几何和拓扑的表征可以提供有效模型设计的基本指导。然而，此类表征的有效性取决于对特征空间适当基向量的识别。本研究的目的是比较和对比两种识别特征空间基向量的基本不同的方法，通过降维来实现。通过这种方式，我们展示了这两种方法如何结合以形成一个联合表征，揭示出单独使用任一方法时均不明显的光谱特性。我们利用AVIRIS-NG反射光谱的多样集合，展示了联合表征在促进雪和冰反射率建模和分类方面的实用性。联合表征还被证明有助于从光谱推断出的物理属性的解读。结合主成分（PCs）和t分布随机近邻嵌入（t-SNEs）的光谱特征空间既提供了代表全球结构的大气圈反射率属性的物理可解释维度，又揭示了局部流形结构，这些结构在全局连续体中未得到解决。联合表征揭示了格陵兰冰盖不同部位的雪-冰川梯度以及冰川和海冰在不同位置共有的冰反射率属性的多个簇。在t-SNE特征空间中揭示出的聚类，以及扩展到联合表征中，区分了雪积累区不同空间位置特有的光谱曲率细微差异，以及与视角几何相关的BRDF效应。PC + t-SNE联合表征能够产生一个物理可解释的光谱特征空间，同时揭示全局拓扑结构，同时保留大气圈超光谱的局部流形结构，这表明此类表征可能扩展到所有陆地覆盖类型的高维超光谱特征空间。