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

Hyperspectral anomalous change detection has been a challenging task for itsemphasis on the dynamics of small and rare objects against the prevalentchanges. In this paper, we have proposed a Multi-Temporal spatial-spectralComparison Network for hyperspectral anomalous change detection (MTC-NET). Thewhole model is a deep siamese network, aiming at learning the prevalentspectral difference resulting from the complex imaging conditions from thehyperspectral images by contrastive learning. A three-dimensional spatialspectral attention module is designed to effectively extract the spatialsemantic information and the key spectral differences. Then the gaps betweenthe multi-temporal features are minimized, boosting the alignment of thesemantic and spectral features and the suppression of the multi-temporalbackground spectral difference. The experiments on the "Viareggio 2013"datasets demonstrate the effectiveness of proposed MTC-NET.

Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection