Non-negative Matrix Factorization Features from Spectral Signatures of AVIRIS Images
Arto Kaarna · 2006
In this study we use non-negative matrix factorization (NMF) in deriving feature vectors from a set of spectral signatures. The purpose is to demonstrate the differences between the NMF and PCA feature vectors. The experiments show that NMF feature vectors are providing local features in spectral domain compared to the holistic features of PCA.