Blind Prediction of Original Image Quality for Sentinel Sar Data
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2019 8th European Workshop on Visual Information Processing (EUVIP) |
| Publisher | IEEE |
| Pages | 105-110 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-7281-4496-2 |
| ISBN (Print) | 978-1-7281-4497-9 |
| DOIs | |
| Publication status | Published - 1 Oct 2019 |
| Publication type | A4 Article in a conference publication |
| Event | European Workshop on Visual Information Processing - Duration: 1 Jan 1900 → … |
Publication series
| Name | European Workshop on Visual Information Processing |
|---|---|
| ISSN (Print) | 2164-974X |
| ISSN (Electronic) | 2471-8963 |
Conference
| Conference | European Workshop on Visual Information Processing |
|---|---|
| Period | 1/01/00 → … |
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