Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or ...
This paper explores the applicability of new sparse algorithms to perform spectral unmixing of hyperspectral images using available spectral libraries instead of resorting to well...
Marian-Daniel Iordache, Antonio J. Plaza, Jos&eacu...
Spectral mixture analysis is an important task for remotely sensed hyperspectral data interpretation. In spectral unmixing, both the determination of spectrally pure signatures (e...
Hyperspectral imaging is a promising tool for applications in geosensing, cultural heritage and beyond. However, compared to current RGB cameras, existing hyperspectral cameras ar...
Rei Kawakami, John Wright, Yu-Wing Tai, Yasuyuki M...
Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Linear spectral unmixing relies on two main steps: 1) identification of pure spectral c...