Details

Autor(en) / Beteiligte

• Charles, A
• Olshausen, B
• Rozell, C J

Titel

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.