Improved Iterative Prediction Reconstruction for Compressive Whiskbroom Imaging

DOI

10.2991/mmebc-16.2016.338How to use a DOI?

Keywords

Hyperspectral Remote Sensing, Whiskbroom Compressive imaging, Reconstruction, Multi-Task Compressive Sensing, Iterative

Abstract

Compressive Sampling is suitable for remote hyperspectral imaging, as it can simplify the architecture of the onboard sensors. The reconstruction process is an indispensable component of the hyperspectral imaging as it decodes the compressive measurements to render a three-dimensional spatio-spectral estimate of the scene. The existed reconstruction methods mainly concentrated in reducing the algorithm complexity and increasing the reconstruction accuracy, not taking into account the sensing paradigm of the onboard sensors, such as compressive whiskbroom imaging. For this reason, an improved iterative prediction reconstruction algorithm employing Multi-Task Compressive Sensing for compressive whiskbroom imaging is proposed. Experimental results run on raw data from AVIRIS confirm the validity of the proposed method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yingbiao Jia
AU  - Zhongliang Luo
PY  - 2016/06
DA  - 2016/06
TI  - Improved Iterative Prediction Reconstruction for Compressive Whiskbroom Imaging
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 1666
EP  - 1670
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.338
DO  - 10.2991/mmebc-16.2016.338
ID  - Jia2016/06
ER  -