Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing
In this paper, we develop a new lossy compression framework for hyperspectral images, termed hyperspectral coded aperture (HYCA), which combines the ideas of spectral unmixing and compressive sensing. It takes advantage of two main properties of hyperspectral data, namely the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. In other words, our proposed approach intends to exploit the fact that the high dimensional hyperspectral data lives in a subspace of much lower dimension due to the mixing phenomenon. Our experimental results, conducted with synthetic hyperspectral data, indicate that the proposed approach represents a promising new strategy.