Back to Search
Start Over
Atrous cGAN for SAR to Optical Image Translation
- Source :
- IEEE Geoscience and Remote Sensing Letters. 19:1-5
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Conditional (cGAN)-based methods proposed so far for synthetic aperture radar (SAR)-to-optical image synthesis tend to produce noisy and unsharp optical outcomes. In this work, we propose the atrous-cGAN, a novel cGAN architecture that improves the SAR-to-optical image translation. The proposed generator and discriminator networks rely on atrous convolutions and incorporate an atrous spatial pyramid pooling (ASPP) module to enhance fine details in the generated optical image by exploiting spatial context at multiple scales. This letter reports experiments carried out to assess the performance of atrous-cGAN for the synthesis of Landsat-8 images from Sentinel-1A data based on three public data sets. The experimental analysis indicated that the atrous-cGAN consistently outperformed the classical pix2pix counterpart in terms of visual quality, similar to the true optical image, and as a feature learning tool for semantic segmentation.
- Subjects :
- Synthetic aperture radar
Spatial contextual awareness
Discriminator
Computer science
business.industry
Pattern recognition
Geotechnical Engineering and Engineering Geology
Translation (geometry)
Image translation
Segmentation
Pyramid (image processing)
Artificial intelligence
Electrical and Electronic Engineering
business
Feature learning
Subjects
Details
- ISSN :
- 15580571 and 1545598X
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Geoscience and Remote Sensing Letters
- Accession number :
- edsair.doi...........bdd52723b72c2fafef97adec50663f7b