Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique
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| Title: | Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique |
|---|---|
| Authors: | Albano, James A. |
| Committee Members: | David Messinger |
| Summary: | Identification of materials from calibrated radiance data collected by an airborne imaging spectrometer depends strongly on the atmospheric and illumination conditions at the time of collection. This thesis demonstrates a methodology for identifying material spectra using the assumption that each unique material class forms a lower-dimensional manifold (surface) in the higher-dimensional spectral radiance space and that all image spectra reside on, or near, these theoretic manifolds. Using a physical model, a manifold characteristic of the target material exposed to varying illumination and atmospheric conditions is formed. A graph-based model is then applied to the radiance data to capture the intricate structure of each material manifold, followed by the application of the commute time distance (CTD) transformation to separate the target manifold from the background. Detection algorithms are then applied in the CTD subspace. This nonlinear transformation is based on a random walk on a graph and is derived from an eigendecomposition of the pseudoinverse of the graph Laplacian matrix. This work provides a geometric interpretation of the CTD transformation, its algebraic properties, the atmospheric and illumination parameters varied in the physics-based model, and the influence the target manifold samples have on the orientation of the coordinate axes in the transformed space. This thesis concludes by demonstrating improved detection results in the CTD subspace as compared to detection in the original spectral radiance space. |
| URL: | https://scholarworks.rit.edu/theses/5951 |
| Database: | OpenDissertations |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: ddu DbLabel: OpenDissertations An: ddu.oai.scholarworks.rit.edu.theses.6956 AccessLevel: 6 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Albano%2C+James+A%2E%22">Albano, James A.</searchLink> – Name: Author Label: Committee Members Group: Au Data: <searchLink fieldCode="CO" term="%22David+Messinger%22">David Messinger</searchLink> – Name: Abstract Label: Summary Group: Ab Data: Identification of materials from calibrated radiance data collected by an airborne imaging spectrometer depends strongly on the atmospheric and illumination conditions at the time of collection. This thesis demonstrates a methodology for identifying material spectra using the assumption that each unique material class forms a lower-dimensional manifold (surface) in the higher-dimensional spectral radiance space and that all image spectra reside on, or near, these theoretic manifolds. Using a physical model, a manifold characteristic of the target material exposed to varying illumination and atmospheric conditions is formed. A graph-based model is then applied to the radiance data to capture the intricate structure of each material manifold, followed by the application of the commute time distance (CTD) transformation to separate the target manifold from the background. Detection algorithms are then applied in the CTD subspace. This nonlinear transformation is based on a random walk on a graph and is derived from an eigendecomposition of the pseudoinverse of the graph Laplacian matrix. This work provides a geometric interpretation of the CTD transformation, its algebraic properties, the atmospheric and illumination parameters varied in the physics-based model, and the influence the target manifold samples have on the orientation of the coordinate axes in the transformed space. This thesis concludes by demonstrating improved detection results in the CTD subspace as compared to detection in the original spectral radiance space. – Name: URL Label: URL Group: URL Data: <link linkTarget="URL" linkTerm="https://scholarworks.rit.edu/theses/5951" linkWindow="_blank">https://scholarworks.rit.edu/theses/5951</link> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ddu&AN=ddu.oai.scholarworks.rit.edu.theses.6956 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English Subjects: – SubjectFull: Remote sensing--Data processing Type: general – SubjectFull: Spectrometer--Data processor Type: general – SubjectFull: Multispectral photography Type: general Titles: – TitleFull: Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Albano, James A. IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 11 Type: published Y: 2013 |
| ResultId | 1 |
