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

Detecting clouds and snow in remote sensing images is an essentialpreprocessing task for remote sensing imagery. Previous works draw inspirationfrom semantic segmentation models in computer vision, with most researchfocusing on improving model architectures to enhance detection performance.However, unlike natural images, the complexity of scenes and the diversity ofcloud types in remote sensing images result in many inaccurate labels in cloudand snow detection datasets, introducing unnecessary noises into the trainingand testing processes. By constructing a new dataset and proposing a noveltraining strategy with the curriculum learning paradigm, we guide the model inreducing overfitting to noisy labels. Additionally, we design a moreappropriate model performance evaluation method, that alleviates theperformance assessment bias caused by noisy labels. By conducting experimentson models with UNet and Segformer, we have validated the effectiveness of ourproposed method. This paper is the first to consider the impact of label noiseon the detection of clouds and snow in remote sensing images.

Learning to detect cloud and snow in remote sensing images from noisy labels