Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 9, Issue 6, Pages 2390-2399Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2016.2541541
Keywords
Abundance estimation; blind compressive sensing (CS); endmember extraction; hyperspectral imaging; linear spectral unmixing; low-dimensional subspace; random embeddings; random projections (RPs)
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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.
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