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

In this paper, we introduce Planet-CR, a benchmark dataset forhigh-resolution cloud removal with multi-modal and multi-resolution datafusion. Planet-CR is the first public dataset for cloud removal to featureglobally sampled high resolution optical observations, in combination withpaired radar measurements as well as pixel-level land cover annotations. Itprovides solid basis for exhaustive evaluation in terms of generating visuallypleasing textures and semantically meaningful structures. With this dataset, weconsider the problem of cloud removal in high resolution optical remote sensingimagery by integrating multi-modal and multi-resolution information. Existingmulti-modal data fusion based methods, which assume the image pairs are alignedpixel-to-pixel, are hence not appropriate for this problem. To this end, wedesign a new baseline named Align-CR to perform the low-resolution SAR imageguided high-resolution optical image cloud removal. It implicitly aligns themulti-modal and multi-resolution data during the reconstruction process topromote the cloud removal performance. The experimental results demonstratethat the proposed Align-CR method gives the best performance in both visualrecovery quality and semantic recovery quality. The project is available athttps://github.com/zhu-xlab/Planet-CR, and hope this will inspire futureresearch.

High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark