Joint Space-Spectrum SG Filtering Algorithms for Hyperspectral Images and Its Application

doi:10.3964/j.issn.1000-0593(2020)12-3699-06

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	Joint Space-Spectrum SG Filtering Algorithms for Hyperspectral Images and Its Application
	H. Z. Ning,X. Tan,Y. H. Li,Q. B. Jiao and W. H. Li
刊名	Spectroscopy and Spectral Analysis
	2020
卷号	40 期号:12 页码:3699-3704
ISSN号	1000-0593
DOI	10.3964/j.issn.1000-0593(2020)12-3699-06
英文摘要	In the process of hyperspectral image( HI) filtering by Savitzky-Golay(SG) filter, the spatial information of HI is ignored which lead to the accuracy of recognition model of wheat HI dataset can only reach 87.0889%. This paper proposes a method, TSG filter, that combines space-spectrum information of HI. By this way, the accuracy of the model is improved by 12.0667% compared with SG filter. The algorithm expands one-dimensional SG convolution core into two-dimensional SG convolution core in four directions. Then the HI data can be quickly convoluted using convolution theorem and fast Fourier transform, so that the space-spectrum noise of HI can quickly filter. When the TSG filter core coefficient m=2 similar to 4, n=3 similar to 5, the SNR of the HI is increased by more than 10%, the PSNR is higher than 30 dB, and the SSIM is greater than 96% which means TSG filter maintains the original HI characteristics well and improve the SNR significantly. After TSG filtering (m=3, n=4) or SG filtering (m=7, n=3), by comparing with the gray image and spectrogram of Pavia University HI, it can be seen that after TSG filtering, the band noise of the image is suppressed, the peak height of the characteristic peaks is increased by 31.68% and the average intensity of the characteristic band is increased by 41.83% while after SG filtering the band noise of the image is still clear and the relative peak height of the characteristic peak is up to 13.40%. The model of wheat HI recognition model based on TSG-PCA-SVM algorithm is constructed. The training set contains 500 sample points and the test set contains 4 500 sample points. The accuracy of this model is as high as 99.1556% and the kappa coefficient is 0.983613 while predicting the test set. The total accuracy predicting all 116 880 samples in the wheat HI data set is as high as 99.2060% which means
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语种	中文
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/64634]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	H. Z. Ning,X. Tan,Y. H. Li,Q. B. Jiao and W. H. Li. Joint Space-Spectrum SG Filtering Algorithms for Hyperspectral Images and Its Application[J]. Spectroscopy and Spectral Analysis,2020,40(12):3699-3704.
APA	H. Z. Ning,X. Tan,Y. H. Li,Q. B. Jiao and W. H. Li.(2020).Joint Space-Spectrum SG Filtering Algorithms for Hyperspectral Images and Its Application.Spectroscopy and Spectral Analysis,40(12),3699-3704.
MLA	H. Z. Ning,X. Tan,Y. H. Li,Q. B. Jiao and W. H. Li."Joint Space-Spectrum SG Filtering Algorithms for Hyperspectral Images and Its Application".Spectroscopy and Spectral Analysis 40.12(2020):3699-3704.