Linked Life Data

19037391

Source:http://linkedlifedata.com/resource/pubmed/id/19037391

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2008-11-27
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1084-7529
pubmed:author
pubmed:issnType
Print
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3001-12
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Tensor methods for hyperspectral data analysis: a space object material identification study.
pubmed:affiliation
Department of Biostatistical Sciences, Wake Forest University Health Sciences, Medical Center Boulevard, Winston-Salem, North Carolina 27109, USA. qizhang@wfubmc.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.