2026-09-19T08:57:35Zhttps://wakespace.lib.wfu.edu/server/oai/request

oai:null:10339/374322026-09-02T11:12:02Zcom_10339_14934col_10339_38132

Zhang, Jiani 2012-09-05T08:35:17Z 2012-09-05T08:35:17Z 2012 https://wakespace.lib.wfu.edu/handle/10339/37432 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. en Wake Forest University Classification Dimension Reduction Hyperspectral Imaging Random Projection Reconstruction SVD RANDOM PROJECTION AND SVD METHODS IN HYPERSPECTRAL IMAGING Thesis Erway, Jennifer Jiang, Miaohua Hu, Xiaofei Pauca, Paul V Mathematics open.access