A comprehensive experimental evaluation of remote sensing hyperspectral image denoising methods for mixed noise

Hyperspectral Imagery (HSI) often suffers from degradation caused by different types of noise, including Gaussian noise, impulse noise, and stripe noise. Restoring the quality of HSI is a complex task, particularly due to the high dimensionality of HSI datasets. In recent years, there has been a gro...

Full description

Saved in:
Bibliographic Details
Main Authors: Joglekar, Maitreyi, Deshpande, Ashwini M.
Format: Conference Proceeding
Language:English
Subjects:
Datasets
Deep learning
Hyperspectral imaging
Image degradation
Image quality
Image restoration
Noise reduction
Qualitative analysis
Random noise
Remote sensing
Tensors
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hyperspectral Imagery (HSI) often suffers from degradation caused by different types of noise, including Gaussian noise, impulse noise, and stripe noise. Restoring the quality of HSI is a complex task, particularly due to the high dimensionality of HSI datasets. In recent years, there has been a growing utilization of low-rank tensor-based and deep learning-based approaches for denoising HSI data, especially in scenarios involving mixed noise. In this research paper, we assess the performance of two low-rank-based methods and one deep learning-based method when applied to benchmark HSI datasets with a focus on the removal of mixed noise. We quantitatively evaluate the restored datasets using a range of quality metrics and subject them to qualitative visual assessment. Our experimental findings reveal that the low-rank-based methods exhibit promising results in effectively mitigating mixed noise in HSI data. These methods offer a viable solution for addressing the challenge of noise removal in hyperspectral imagery, contributing to improved data quality and usability.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0227626