RANDOM PROJECTION AND SVD METHODS IN HYPERSPECTRAL IMAGING
Electronic Theses and Dissertations
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Item Details
- abstract
- Hyperspectral imaging provides researchers with abundant information with which to study the characteristics of objects in a scene. Processing the massive hyperspectral imagery datasets in a way that efficiently provides useful information becomes an important issue. In this thesis, we consider methods which reduce the dimension of hyperspectral data while retaining as much useful information as possible.
- subject
- Classification
- Dimension Reduction
- Hyperspectral Imaging
- Random Projection
- Reconstruction
- SVD
- contributor
- Erway, Jennifer (committee chair)
- Jiang, Miaohua (committee member)
- Hu, Xiaofei (committee member)
- Pauca, Paul V (committee member)
- date
- 2012-09-05T08:35:17Z (accessioned)
- 2012-09-05T08:35:17Z (available)
- 2012 (issued)
- degree
- Mathematics (discipline)
- identifier
- http://hdl.handle.net/10339/37432 (uri)
- language
- en (iso)
- publisher
- Wake Forest University
- title
- RANDOM PROJECTION AND SVD METHODS IN HYPERSPECTRAL IMAGING
- type
- Thesis