Hyperspectral feature spaces are useful for many remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In such cases, characterization of the feature space dimensionality, geometry and topology can provide guidance for effective model design. The objective of this study is to compare and contrast two approaches for identifying feature space basis vectors via dimensionality reduction. These 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 the snow-firn-ice continuum to illustrate the utility of joint characterization and identify physical properties inferred from the spectra. Spectral feature spaces combining principal components (PCs) and t-distributed Stochastic Neighbor Embeddings (t-SNEs) provide physically interpretable dimensions representing the global (PC) structure of cryospheric reflectance properties and local (t-SNE) manifold structures revealing clustering not resolved in the global continuum. 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. Clustering revealed in t-SNE feature spaces, and extended to the joint characterization, distinguishes differences in spectral curvature specific to location within the snow accumulation zone, and BRDF effects related to view geometry. The ability of PC+t-SNE joint characterization to produce a physically interpretable spectral feature spaces revealing global topology while preserving local manifold structures suggests that this characterization might be extended to the much higher dimensional hyperspectral feature space of all terrestrial land cover.
翻译:超光谱特征空间可用于从光谱混合物建模到离散专题分类等许多遥感应用,从光谱混合物建模到离散专题分类,在这些情况下,对地貌空间维度、几何学和地形学特征的定性可以为有效的模型设计提供指导。本研究的目的是比较和对比两种方法,以通过减少维度来识别地貌基向量矢量的地貌矢量。这些方法可以结合起来,使共同特征能够显示光谱特性,而光谱特性仅使用两种方法都不明显。我们使用不同系列的“AVIRIS-NG”反射自雪-纤维-冰层连续的光谱谱谱谱谱谱谱谱谱,以说明从光谱中推断出的联合地貌特征和物理特性的物理特性。 将主要组成部分(PC)和T-SDBE(t-SNEE)相交集的地貌和多层地貌地貌地貌结构的地貌结构地貌和地表层的地貌、地貌和多层的地表层的地貌、地貌、地貌、地表层的地貌、地貌、地貌和地表层的地貌的地貌、地貌、地貌的地貌、地貌、地貌、地貌的地貌、地貌、地貌的地貌、地貌、地貌的地貌、地貌、地貌、地貌、地貌的地貌的地貌的地貌、地貌的地貌的地貌的地貌的地貌的地貌、地貌的地貌的地貌的地貌的地貌的地貌、和地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌、和地貌、和地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌、和地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的地貌的