Joint Characterization of the Cryospheric Spectral Feature Space
Joint Characterization of the Cryospheric Spectral Feature Space
复制标题
冰冻圈光谱特征空间的联合表征
DOI：
10.3389/frsen.2021.793228
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
D. Sousa
中科院分区：
文献类型：
--
作者：
C. Small;D. Sousa
多光谱和高光谱特征空间对于从光谱混合建模到离散主题分类的各种遥感应用是有用的。在许多这样的应用中，模型被用来将反射率(或辐射率)的高维连续统投影到图像目标的物理属性或范畴组成的低维映射上。在这种情况下，特征空间的维度、几何和拓扑的表征可以为有效的模型设计提供基本的指导。然而，这种表征的实用性取决于识别用于特征空间的适当的基向量。本研究的目的是比较和对比两种通过降维识别特征空间基矢量的根本不同的方法。在这样做的过程中，我们说明了如何将这两种方法结合起来，以呈现联合表征，从而揭示单独使用这两种方法中的任何一种都不明显的光谱特性。我们使用不同的AVIRIS-NG冰雪反射率光谱集合来说明联合表征的实用性，以便于对雪和冰的反射率进行建模和分类。联合表征也被证明有助于解释从光谱推断的物理性质。结合主成分(PCS)和t分布随机邻近嵌入(t-SNES)的光谱特征空间既提供了物理上可解释的维度，表示了冰冻层反射特性的全局结构，也提供了揭示不能在全球连续统中分辨的聚类的局部流形结构。联合特征显示了格陵兰冰盖不同部分积雪梯度的明显连续性，以及不同位置的冰川和海冰共同的多个冰反射特性簇。在t-SNE特征空间中显示的聚集，并扩展到联合表征，区分了积雪堆积带内不同空间位置特定的光谱曲率的细微差异，以及与视图几何有关的BRDF效应。PC+t-SNE联合刻画能够产生物理上可解释的光谱特征空间，揭示全球拓扑结构，同时保持冰冻层高光谱的局部流形结构，这表明这种类型的刻画可以扩展到所有陆地覆盖的高维高光谱特征空间。
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.