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Sensors 2017, 17(6), 1322; doi:10.3390/s17061322

Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data

College of Life Information Science & Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
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Authors to whom correspondence should be addressed.
Academic Editor: Lammert Kooistra
Received: 30 March 2017 / Revised: 26 May 2017 / Accepted: 1 June 2017 / Published: 7 June 2017
(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

Abstract

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improve data storage, transmission, and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. The spectral adaptive grouping distributed compressive sensing (AGDCS) algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The spectral characteristics of hyperspectral data are analyzed and the joint sparse model is constructed. The spectral bands are adaptively grouped and the hyperspectral data are compressed and reconstructed on the basis of grouping. The experimental results showed that, compared with orthogonal matching pursuit (OMP) and gradient projection for sparse reconstruction (GPSR), AGDCS can significantly improve the visual effect of image reconstruction in the spatial domain. The peak signal-to-noise ratio (PSNR) at a low sampling rate (the sampling rate is lower than 0.2) increases by 13.72 dB than OMP and 1.66 dB than GPSR. In the spectral domain, the average normalized root mean square error, the mean absolute percentage error, and the mean absolute error of AGDCS is 35.38%, 31.83%, and 33.33% lower than GPSR, respectively. Additionally, AGDCS can achieve relatively high reconstructed efficiency. View Full-Text
Keywords: hyperspectral image; spectral characteristics of plants; spectral adaptive grouping; compressive sensing hyperspectral image; spectral characteristics of plants; spectral adaptive grouping; compressive sensing
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MDPI and ACS Style

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

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