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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Conditional Generative Adversarial Nets (CGANs) need a significantly hugedataset with a detailed pixel-wise annotation to generate high-quality images.Unfortunately, any amount of missing pixel annotations may significantly impactthe result not only locally, but also in annotated areas. To the best of ourknowledge, such a challenge has never been investigated in the broader field ofGANs. In this paper, we take the first step in this direction to study theproblem of CGAN-based satellite image synthesis given partially annotatedimages. We first define the problem of image synthesis using partiallyannotated data, and we discuss a scenario in which we face such a challenge. Wethen propose an effective solution called detail augmentation to address thisproblem. To do so, we tested two different approaches to augment details tocompensate for missing pixel-wise annotations. In the first approach, weaugmented the original images with their Canny edges to using the CGAN tocompensate for the missing annotations. The second approach, however, attemptedto assign a color to all pixels with missing annotation. Eventually, adifferent CGAN was trained to translate the new feature images into a finaloutput.

Towards CGAN-based Satellite Image Synthesis with Partial Pixel-Wise Annotation