English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is afundamental task in remote sensing that can enrich the dataset by fusinginformation from different sources. Recently, many methods have been proposedto tackle this task, but they are still difficult to complete the conversionfrom low-resolution images to high-resolution images. Thus, we propose aframework, SAR2EO, aiming at addressing this challenge. Firstly, to generatehigh-quality EO images, we adopt the coarse-to-fine generator, multi-scalediscriminators, and improved adversarial loss in the pix2pixHD model toincrease the synthesis quality. Secondly, we introduce a denoising module toremove the noise in SAR images, which helps to suppress the noise whilepreserving the structural information of the images. To validate theeffectiveness of the proposed framework, we conduct experiments on the datasetof the Multi-modal Aerial View Imagery Challenge (MAVIC), which consists oflarge-scale SAR and EO image pairs. The experimental results demonstrate thesuperiority of our proposed framework, and we win the first place in the MAVICheld in CVPR PBVS 2023.

SAR2EO: A High-resolution Image Translation Framework with Denoising Enhancement