Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Sparse coding for spectral signatures in hyperspectral images
Ist Teil von
2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010, p.191-195
Ort / Verlag
IEEE
Erscheinungsjahr
2010
Quelle
IEEE Xplore
Beschreibungen/Notizen
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.