Joint Characterization of the Cryospheric Spectral Feature Space
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 obj...
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ftdatacite:10.48550/arxiv.2112.01416 2023-05-15T16:21:32+02:00 Joint Characterization of the Cryospheric Spectral Feature Space Small, Christopher Sousa, Daniel 2021 https://dx.doi.org/10.48550/arxiv.2112.01416 https://arxiv.org/abs/2112.01416 unknown arXiv Creative Commons Attribution Non Commercial No Derivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode cc-by-nc-nd-4.0 CC-BY-NC-ND Geophysics physics.geo-ph Machine Learning stat.ML FOS Physical sciences FOS Computer and information sciences Article CreativeWork article Preprint 2021 ftdatacite https://doi.org/10.48550/arxiv.2112.01416 2022-03-10T13:14:54Z 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. : 30 pages, 11 figures Article in Journal/Newspaper glacier Greenland Ice Sheet Sea ice DataCite Metadata Store (German National Library of Science and Technology) Greenland |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Geophysics physics.geo-ph Machine Learning stat.ML FOS Physical sciences FOS Computer and information sciences |
spellingShingle |
Geophysics physics.geo-ph Machine Learning stat.ML FOS Physical sciences FOS Computer and information sciences Small, Christopher Sousa, Daniel Joint Characterization of the Cryospheric Spectral Feature Space |
topic_facet |
Geophysics physics.geo-ph Machine Learning stat.ML FOS Physical sciences FOS Computer and information sciences |
description |
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. : 30 pages, 11 figures |
format |
Article in Journal/Newspaper |
author |
Small, Christopher Sousa, Daniel |
author_facet |
Small, Christopher Sousa, Daniel |
author_sort |
Small, Christopher |
title |
Joint Characterization of the Cryospheric Spectral Feature Space |
title_short |
Joint Characterization of the Cryospheric Spectral Feature Space |
title_full |
Joint Characterization of the Cryospheric Spectral Feature Space |
title_fullStr |
Joint Characterization of the Cryospheric Spectral Feature Space |
title_full_unstemmed |
Joint Characterization of the Cryospheric Spectral Feature Space |
title_sort |
joint characterization of the cryospheric spectral feature space |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2112.01416 https://arxiv.org/abs/2112.01416 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
glacier Greenland Ice Sheet Sea ice |
genre_facet |
glacier Greenland Ice Sheet Sea ice |
op_rights |
Creative Commons Attribution Non Commercial No Derivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode cc-by-nc-nd-4.0 |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.48550/arxiv.2112.01416 |
_version_ |
1766009536142901248 |