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

Deep learning (DL)-based methods have recently shown great promise inbitemporal change detection (CD). However, most existing methods areineffective in simultaneously capturing long-range dependencies and exploitinglocal spatial information, resulting in inaccurate CD maps with discerningedges. To overcome these obstacles, a novel Denoising Diffusion ProbabilisticModel (DDPM)-based generative CD approach called GCD-DDPM is proposed forremote sensing data. More specifically, GCD-DDPM is designed to directlygenerate CD maps by leveraging variational inference, which enables GCD-DDPM toaccurately distinguish subtle and irregular buildings or natural scenes fromthe background. Furthermore, an adaptive calibration conditional differenceencoding technique is proposed for GCD-DDPM to enhance the CD map throughguided sampling of the differences among multi-level features. Finally, a noisesuppression-based semantic enhancer (NSSE) is devised to cope with thehigh-frequency noise incurred in the CD map by capitalizing on the priorknowledge derived from the current step. Extensive experiments on four CDdatasets, namely CDD, WHU, Levier and GVLM, confirm the good performance of theproposed GCD-DDPM.

GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM