Satellite multispectral and hyperspectral image de-noising with enhanced adaptive generalized gaussian distribution threshold in the wavelet domain

Noorbakhsh Amiri Golilarz, Hui Gao*, Saied Pirasteh, Mohammad Yazdi, Junlin Zhou, Yan Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
149 Downloads (Pure)

Abstract

The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. Noise suppression based on thresholding neural network (TNN) and optimization algorithms perform well in de-noising. However, there are some problems that need to be addressed. Furthermore, finding the optimal threshold value is a challenging task for learning algorithms. Moreover, in an optimization-based noise removal technique, we must utilize the optimization algorithm to overcome the problem. These methods are effective at reducing noise but may blur some parts of an image, and they are time-consuming. This flaw motivated the authors to develop an efficient de-noising method to discard un-wanted noises from these images. This study presents a new enhanced adaptive generalized Gaussian distribution (AGGD) threshold for satellite and hyperspectral image (HSI) de-noising. This function is data-driven, non-linear, and it can be fitted to any image. Applying this function provides us with an optimum threshold value without using any least mean square (LMS) learning or optimization algorithms. Thus, it is possible to save the processing time as well. The proposed function contains two main parts. There is an AGGD threshold in the interval [−σn, σn ], and a new non-linear function behind the interval. These combined functions can tune the wavelet coefficients properly. We applied the proposed technique to various satellite remote sensing images. We also used hyperspectral remote sensing images from AVIRIS, HYDICE, and ROSIS sensors for our experimental analysis and validation process. We applied peak signal-to-noise ratio (PSNR) and Mean Structural Similarity Index (MSSIM) to measure and evaluate the performance analysis of different de-noising techniques. Finally, this study shows the superiority of the developed method as compared with the previous TNN and optimization-based noise suppression methods. Moreover, as the results indicate, the proposed method improves PSNR values and visual inspection significantly when compared with various image de-noising methods.

Original languageEnglish
Article number101
Pages (from-to)1-16
Number of pages16
JournalRemote Sensing
Volume13
Issue number1
DOIs
Publication statusPublished - 1 Jan 2021
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • adaptive
  • enhanced AGGD
  • image de-noising
  • optimization algorithm
  • remote sensing
  • TNN
  • AVIRIS sensor
  • HYDICE sensor
  • ROSIS sensor

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