title: Tensor methods for hyperspectral data analysis: a space object material identification study.
abstract: An important and well-studied problem in hyperspectral image data applications is to identify materials present in the object or scene being imaged and to quantify their abundance in the mixture. Due to the increasing quantity of data usually encountered in hyperspectral datasets, effective data compression is also an important consideration. In this paper, we develop novel methods based on tensor analysis that focus on all three of these goals: material identification, material abundance estimation, and data compression. Test results are reported in all three perspectives.
authors.affiliation: Department of Biostatistical Sciences, Wake Forest University Health Sciences, Medical Center Boulevard, Winston-Salem, North Carolina 27109, USA. qizhang@wfubmc.edu
authors.forename: Qiang
authors.initials: Q
authors.lastname: Zhang
authors.forename: Han
authors.initials: H
authors.lastname: Wang
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Plemmons
authors.forename: V Pau'l
authors.initials: VP
authors.lastname: Pauca
mesh_terms: D000465:Algorithms; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008422:Materials Testing / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018531:Spacecraft; D013057:Spectrum Analysis / Q000379:methods*