Study on sparse representation and measurement matrices of compressive sensing of plant hyperspectral data for retrieving plant physiological and biochemical parameters. (June 2019)

• Record Type: Journal Article
• Authors: Xu, Ping; Cao, Yue; Xue, Lingyun; Zhang, Jingcheng; Zhu, Lei; Chen, Bingqiang; Ma, Fengjuan
• Is Part Of: Biosystems engineering. Volume 182(2019)
• Page Start: 38
• Page End: 53
• Publication Date: 2019-06
• Subjects: Remote sensing -- Compressive sensing -- Physiological and biochemical parameters -- Spectral index -- Sparse representation -- Measurement matrix; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2019.02.011
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : The sparse representation of the original signal and compression of the sparse coefficients in the process of compressive sensing have a large influence on the reconstruction of plant hyperspectral data to retrieve plant physiological and biochemical parameters. In order to compress plant hyperspectral data more effectively, we should retain the non-redundant information of the original plant hyperspectral data which lays a good basis for spectral data recovery. Based on the compressive sensing of plant spectral data, discrete cosine transform (DCT), fast Fourier transform (FFT) and K-singular value decomposition (K-SVD) dictionaries are used to compress and reconstruct the plant spectra at different sampling. After the spectral curve, the error of spectral indices of the reconstructed data and the error of inversion model are evaluated, and experimental results show that the K-SVD dictionary can achieve better sparsity performance than that of the other dictionaries at different sampling rates. Based on the K-SVD dictionary, Gaussian matrix, Bernoulli matrix, partial Fourier matrix, sparse random matrix, Toeplitz matrix, and cycling matrix are used to compress and reconstruct the plant spectra at different sampling rates. Experimental results show that partial Fourier matrix can achieve the better compression performance of the spectral curve, SAM, spectral index error and the reconstructed mean PSNR values than that of the other measurement matrices. Therefore, the K-SVD dictionary and partial Fourier matrix of the compressive sensing show the best reconstructed efficiency. Highlights: Sparse representation and measurement matrices of compressive sensing are evaluated. The compression performance is evaluated on spectral, feature and model levels. Key physiological and biochemical parameters are chosen to test the retrieving efficacy. K-SVD with Gaussian, Bernoulli, and Part Fourier matrices achieve good reconstructed performance.