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

Anomaly segmentation in high spatial resolution (HSR) remote sensing imageryis aimed at segmenting anomaly patterns of the earth deviating from normalpatterns, which plays an important role in various Earth vision applications.However, it is a challenging task due to the complex distribution and theirregular shapes of objects, and the lack of abnormal samples. To tackle theseproblems, an anomaly segmentation model based on pixel descriptors (ASD) isproposed for anomaly segmentation in HSR imagery. Specifically, deep one-classclassification is introduced for anomaly segmentation in the feature space withdiscriminative pixel descriptors. The ASD model incorporates the data argumentfor generating virtual ab-normal samples, which can force the pixel descriptorsto be compact for normal data and meanwhile to be diverse to avoid the modelcollapse problems when only positive samples participated in the training. Inaddition, the ASD introduced a multi-level and multi-scale feature extractionstrategy for learning the low-level and semantic information to make the pixeldescriptors feature-rich. The proposed ASD model was validated using four HSRdatasets and compared with the recent state-of-the-art models, showing itspotential value in Earth vision applications.

Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors