A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing

Authors:
Chengbo Li;Ting Sun;Kevin F. Kelly;Yin Zhang
Affiliations:
Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA;Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA;Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA;Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
Venue:
IEEE Transactions on Image Processing
Year:
2012

Citing 0
Cited 2

Randomized SVD methods in hyperspectral imaging

Journal of Electrical and Computer Engineering - Special issue on Algorithms for Multispectral and Hyperspectral Image Analysis
Compressive sensing and adaptive direct sampling in hyperspectral imaging

Digital Signal Processing

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Abstract

Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation, and input/output throughputs, particularly when real-time processing is desired. In this paper, a proof-of-concept study is conducted on compressive sensing (CS) and unmixing for hyperspectral imaging. Specifically, we investigate a low-complexity scheme for hyperspectral data compression and reconstruction. In this scheme, compressed hyperspectral data are acquired directly by a device similar to the single-pixel camera based on the principle of CS. To decode the compressed data, we propose a numerical procedure to compute directly the unmixed abundance fractions of given endmembers, completely bypassing high-complexity tasks involving the hyperspectral data cube itself. The reconstruction model is to minimize the total variation of the abundance fractions subject to a preprocessed fidelity equation with a significantly reduced size and other side constraints. An augmented Lagrangian-type algorithm is developed to solve this model. We conduct extensive numerical experiments to demonstrate the feasibility and efficiency of the proposed approach, using both synthetic data and hardware-measured data. Experimental and computational evidences obtained from this paper indicate that the proposed scheme has a high potential in real-world applications.