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

Earth observing satellites carrying multi-spectral sensors are widely used tomonitor the physical and biological states of the atmosphere, land, and oceans.These satellites have different vantage points above the earth and differentspectral imaging bands resulting in inconsistent imagery from one to another.This presents challenges in building downstream applications. What if we couldgenerate synthetic bands for existing satellites from the union of all domains?We tackle the problem of generating synthetic spectral imagery formultispectral sensors as an unsupervised image-to-image translation problemwith partial labels and introduce a novel shared spectral reconstruction loss.Simulated experiments performed by dropping one or more spectral bands showthat cross-domain reconstruction outperforms measurements obtained from asecond vantage point. On a downstream cloud detection task, we show thatgenerating synthetic bands with our model improves segmentation performancebeyond our baseline. Our proposed approach enables synchronization ofmultispectral data and provides a basis for more homogeneous remote sensingdatasets.

Spectral Synthesis for Satellite-to-Satellite Translation