Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing

doi:10.3389/fpls.2021.627865

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	Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing
	Y. H. Li; X. Tan; W. Zhang; Q. B. Jiao; Y. X. Xu; H. Li; Y. B. Zou; L. Yang and Y. P. Fang
	2021
发表期刊	Frontiers in Plant Science
ISSN	1664-462X
卷号	12 页码:11
摘要	This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images. Firstly, the images were filtered and segmented by using spectral angle and principal component analysis, and the segmented results are intersected and then used to mask the hyperspectral images. Hyperspectral images with a excellent segmentation result was obtained. Secondly, the standard reflectance plates with reflectance of 2 and 98% were used as a priori spectral information for image correction of samples with known true spectral information. The mean square error between the corrected and calibrated spectra is less than 0.0001. Comparing with the black-and-white correction method, the classification model constructed based on this method has higher classification accuracy. Finally, the convolution kernel of the one-dimensional Savitzky-Golay (SG) filter was extended into a two-dimensional convolution kernel to perform joint spatial-spectral dimensional filtering (TSG) on the hyperspectral images. The SG filter (m = 7,n = 3) and TSG filter (m = 3,n = 4) were applied to the hyperspectral image of Pavia University and the quality of the hyperspectral image was evaluated. It was found that the TSG filter retained most of the original features while the noise information of the filtered hyperspectral image was less. The hyperspectral images of sample 1-1 and sample 1-2 were processed by the image segmentation and image correction methods proposed in this paper. Then the classification models based on SG filtering and TSG filtering hyperspectral images were constructed, respectively. The results showed that the TSG filter-based model had higher classification accuracy and the classification accuracy is more than 98%.
DOI	10.3389/fpls.2021.627865
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收录类别	SCI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65587
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	Y. H. Li,X. Tan,W. Zhang,et al. Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing[J]. Frontiers in Plant Science,2021,12:11.
APA	Y. H. Li.,X. Tan.,W. Zhang.,Q. B. Jiao.,Y. X. Xu.,...&L. Yang and Y. P. Fang.(2021).Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing.Frontiers in Plant Science,12,11.
MLA	Y. H. Li,et al."Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing".Frontiers in Plant Science 12(2021):11.

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