Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing

doi:10.3389/fpls.2021.627865

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	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
刊名	Frontiers in Plant Science
	2021
卷号	12 页码:11
ISSN号	1664-462X
DOI	10.3389/fpls.2021.627865
英文摘要	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%.
URL标识	查看原文
内容类型	期刊论文
源URL	[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.