Abstract
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%.
Highlights
IntroductionHyperspectral imaging technology has been developed increasingly mature
In recent years, hyperspectral imaging technology has been developed increasingly mature
Where θ represents the spectral information of the pixel and the angle between the x-axis in two-dimensional plane coordinates of the hyperspectral image, which is called the spectral angle in this paper; t represents the spectral information of the pixel; and r represents the unit vector of the x-axis
Summary
Hyperspectral imaging technology has been developed increasingly mature. Preprocessing operations are required before classifying hyperspectral images. The black-and-white correction method (Esfahani et al, 2020; Xin et al, 2020) is the most widely used method for the correction of noise in dark currents. Because the hyperspectral image is a two-dimensional image in the spatial dimension, the individual bands can be denoised using classical image denoising methods; the spectral dimension is often denoised using SavitzkyGolay (SG) filtering (Wang et al, 2019; Ayaz et al, 2020; Jiang et al, 2020; Khamsopha and Teerachaichayut, 2020; Li et al, 2020; Ye et al, 2020)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
