Hyperspectral Blind Reconstruction From Random Spectral Projections

Devising the right hardware for hyperspectral imaging thanks to a clear path from reality to image reconstruction is what the authors of the following paper enable:

Hyperspectral Blind Reconstruction From Random Spectral Projections by Gabriel Martín , José Bioucas-Dias

This paper proposes a blind hyperspectral reconstruction technique termed spectral compressive acquisition (SpeCA) conceived to spaceborne sensors systems which are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. SpeCA exploits the fact that hyperspectral vectors often belong to a low-dimensional subspace and it is blind in the sense that the subspace is learned from the measured data. SpeCA encoder is computationally very light; it just computes random projections (RPs) with the acquired spectral vectors. SpeCA decoder solves a form of blind reconstruction from RPs whose complexity, although higher than that of the encoder, is very light in the sense that it requires only the modest resources to be implemented in real time. SpeCA coding/decoding scheme achieves perfect reconstruction in noise-free hyperspectral images (HSIs) and is very competitive in noisy data. The effectiveness of the proposed methodology is illustrated in both synthetic and real scenarios.

earlier:
Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)
Jun Li, Jose M. Bioucas-Dias, Antonio Plaza, Lin Liu

The recently introduced collaborative nonnegative matrix factorization (CoNMF) algorithm was conceived to simultaneously estimate the number of endmembers, the mixing matrix, and the fractional abundances from hyperspectral linear mixtures. This paper introduces R-CoNMF, which is a robust version of CoNMF. The robustness has been added by a) including a volume regularizer which penalizes the distance to a mixing matrix inferred by a pure pixel algorithm; and by b) introducing a new proximal alternating optimization (PAO) algorithm for which convergence to a critical point is guaranteed. Our experimental results indicate that R-CoNMF provides effective estimates both when the number of endmembers are unknown and when they are known.

  Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.