NDLI: Sparse Analysis Based on Generalized Gaussian Model for Spectrum Recovery With Compressed Sensing Theory

Content Provider	IEEE Xplore Digital Library
Author	Jihao Yin Jianying Sun Xiuping Jia
Copyright Year	2008
Abstract	Imaging spectrometers can supply spatial data in abundant narrow and continuous wavelength bands. However, the huge data volume produced encounters the difficulty in storage and transmission. On the other hand, these hyperspectral data sets contain high redundancy, which offers an opportunity to reduce the number of spectral measurements and recover the full spectrum from limited samples without losing principal spectral information. This paper addresses the application of compressed sensing (CS) theory to hyperspectral data reconstruction. An important question involved is how to know a spectrum is sparse enough so that CS can be applied effectively. We provide a quantitative answer and develop a strategy to measure the degree of sparsity of a spectrum based on the generalized Gaussian distribution (GGD) model. The novelty includes the derivation of the sharpness of the GGD and how to estimate the sharpness of a spectral signal. The proposed strategy was tested using the spectral data from USGS database and an AVIRIS-HSI data set. The results demonstrate that it is important to introduce the sparsity measure, as CS offers a high reconstruction rate and low relative errors compared with the existing methods for sparse signals only.
Starting Page	2752
Ending Page	2759
Page Count	8
File Size	1944032
File Format	PDF
ISSN	19391404
Volume Number	8
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Interpolation Image reconstruction Compressed sensing Standards sparse analysis Compressed sensing (CS) generalized Gaussian distribution (GGD) hyperspectral image (HSI)
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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