A Spectral-spatial Cooperative Noise-evaluation Method for Hyperspectral Imaging

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attent...

Full description

Saved in:
Bibliographic Details
Published in:Current optics and photonics 2020, 4(6), , pp.530-539
Main Authors: Zhou, Bing, Li, Bingxuan, He, Xuan, Liu, Hexiong
Format: Article
Language:English
Subjects:
Algorithms
Feature extraction
Hyperspectral imaging
Image processing
Noise
Parameter estimation
Regression models
Spatial data
Spectra
물리학
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise- evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.
ISSN:2508-7266
2508-7274
DOI:10.3807/COPP.2020.4.6.530