Preprint Article Version 1 This version is not peer-reviewed

Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data

Version 1 : Received: 6 May 2017 / Approved: 8 May 2017 / Online: 8 May 2017 (11:45:34 CEST)

A peer-reviewed article of this Preprint also exists.

Xu, P.; Liu, J.; Xue, L.; Zhang, J.; Qiu, B. Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data. Sensors 2017, 17, 1322. Xu, P.; Liu, J.; Xue, L.; Zhang, J.; Qiu, B. Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data. Sensors 2017, 17, 1322.

Journal reference: Sensors 2017, 17, 1322
DOI: 10.3390/s17061322

Abstract

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improving data storage, transmission and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. By introducing the idea of compressive sensing in compressed reconstruction, the spectral adaptive grouping distributed compressive sensing algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The experimental results showed that comparing with orthogonal matching pursuit(OMP) and gradient projection reconstruction(GPSR), the proposed algorithm can significantly improve the visual effect of image reconstruction in the spatial domain. The PSNR in low sampling rate(sampling rate is lower than 0.2) increases by 13.72dB than OMP and 1.66dB than GPSR. In the spectral domain, the average normalized root mean square error、the mean absolute percentage error and the mean absolute error of the proposed algorithm is35.38%,31.83% and 33.33% lower than GPSR respectively.. Therefore, the proposed algorithm can achieve relatively high reconstructed efficiency.

Subject Areas

hyperspectral image; spectral characteristics of plants; spectral adaptive grouping; compressive sensing

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