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Blind Prediction of Original Image Quality for Sentinel Sar Data

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 8th European Workshop on Visual Information Processing (EUVIP)
PublisherIEEE
Pages105-110
Number of pages6
ISBN (Electronic)978-1-7281-4496-2
ISBN (Print)978-1-7281-4497-9
DOIs
Publication statusPublished - 1 Oct 2019
Publication typeA4 Article in a conference publication
EventEuropean Workshop on Visual Information Processing -
Duration: 1 Jan 1900 → …

Publication series

NameEuropean Workshop on Visual Information Processing
ISSN (Print)2164-974X
ISSN (Electronic)2471-8963

Conference

ConferenceEuropean Workshop on Visual Information Processing
Period1/01/00 → …

Abstract

Synthetic aperture radar (SAR) images are often subject to visual inspection and analysis. Many factors impact on visual quality of SAR images, such as properties of speckle, dynamic range of data, etc. Thus, the corresponding metrics have to be applied and it is worth predicting their values before one starts analyzing images. Using a set of input parameters (both statistical and spectral) and a trained neural network (NN), we show that full-reference visual quality metrics can be predicted for images acquired by modern SAR Sentinel-1. A prediction accuracy is studied and verified on real-life examples. The source codes and datasets will be made publicly available at https://github.com/asrubel/EUVIP2019.

Keywords

  • Image quality, prediction algorithms, synthetic aperture radar, multi-layer neural network, speckle

Publication forum classification

Field of science, Statistics Finland