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Abundance Estimation of Hyperspectral Data with Low Compressive Sampling Rate

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Abstract

Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation, and I/O throughputs. In this paper, a compressive sensing framework with low sampling rate is described for hyperspectral imagery. It is based on the widely used linear spectral mixture model. Abundance fractions can be calculated directly from compressively sensed data with no need to reconstruct original hyperspectral imagery. The proposed abundance estimation model is based on the sparsity of abundance fractions and an alternating direction method of multipliers is developed to solve this model. Experiments show that the proposed scheme has a high potential to unmix compressively sensed hyperspectral data with low sampling rate.

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Acknowledgements

This work was supported by the Key Projects of Natural Science Research of Universities of Anhui Province under Grant KJ2016A884, Quality Engineering Project of Universities of Anhui Province under Grant 2016zy126 and the National Natural Science Foundation of China under Grant 61071171.

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Authors and Affiliations

  1. Department of Electric Engineering, Tongling University, Tongling, 244000, Anhui, China

    Zhongliang Wang

  2. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710129, Shaanxi, China

    Zhongliang Wang & Yan Feng

Authors
  1. Zhongliang Wang
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  2. Yan Feng
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Correspondence to Yan Feng.

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This article is part of the Topical Collection on Hyperspectral Imaging and Image Processing.

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Wang, Z., Feng, Y. Abundance Estimation of Hyperspectral Data with Low Compressive Sampling Rate. Sens Imaging 18, 23 (2017). https://doi.org/10.1007/s11220-017-0168-5

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  • DOI: https://doi.org/10.1007/s11220-017-0168-5

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