2020
DOI: 10.3390/rs12081324
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Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression

Abstract: HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR (multiple linear regression) is proposed for the above mixed noise to estimate the noise standard deviation of both SI component and SD component. First, superpixel segmentation is… Show more

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Cited by 4 publications
(3 citation statements)
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“…For hyperspectral images, images in each band can be treated as a single static image [17], with different sizes of noise in different bands.…”
Section: Hyperspectral Image Noise Characteristicsmentioning
confidence: 99%
“…For hyperspectral images, images in each band can be treated as a single static image [17], with different sizes of noise in different bands.…”
Section: Hyperspectral Image Noise Characteristicsmentioning
confidence: 99%
“…Continuing the presentation of the papers of the special issue, two of them [3,4] consider the blurring effects and noise introduced into the images by the acquisition system.…”
mentioning
confidence: 99%
“…Regarding [4], the authors propose a superpixel-based noise estimation algorithm suitable for hyperspectral images acquired by new-generation hyperspectral sensors. For this reason, the methodology considers both electronic and photon noise.…”
mentioning
confidence: 99%