TY - GEN
T1 - Sparse coding for spectral signatures in hyperspectral images
AU - Charles, Adam
AU - Olshausen, Bruno
AU - Rozell, Christopher J.
PY - 2010
Y1 - 2010
N2 - The growing use of hyperspectral imagery lead us to seek automated algorithms for extracting useful information about the scene. Recent work in sparse approximation has shown that unsupervised learning techniques can use example data to determine an efficient dictionary with few a priori assumptions. We apply this model to sample hyperspectral data and show that these techniques learn a dictionary that: 1) contains a meaningful spectral decomposition for hyperspectral imagery, 2) admit representations that are useful in determining properties and classifying materials in the scene, and 3) forms local approximations to the nonlinear manifold structure present in the actual data.
AB - The growing use of hyperspectral imagery lead us to seek automated algorithms for extracting useful information about the scene. Recent work in sparse approximation has shown that unsupervised learning techniques can use example data to determine an efficient dictionary with few a priori assumptions. We apply this model to sample hyperspectral data and show that these techniques learn a dictionary that: 1) contains a meaningful spectral decomposition for hyperspectral imagery, 2) admit representations that are useful in determining properties and classifying materials in the scene, and 3) forms local approximations to the nonlinear manifold structure present in the actual data.
KW - Array Processing and Statistical Signal Processing
KW - E.4
KW - Remote Sensing
UR - http://www.scopus.com/inward/record.url?scp=79958005305&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79958005305&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2010.5757496
DO - 10.1109/ACSSC.2010.5757496
M3 - Conference contribution
AN - SCOPUS:79958005305
SN - 9781424497218
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 191
EP - 195
BT - Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
T2 - 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Y2 - 7 November 2010 through 10 November 2010
ER -