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

Change detection in heterogeneous multitemporal satellite images is achallenging and still not much studied topic in remote sensing and earthobservation. This paper focuses on comparison of image pairs covering the samegeographical area and acquired by two different sensors, one optical radiometerand one synthetic aperture radar, at two different times. We propose aclustering-based technique to detect changes, identified as clusters that splitor merge in the different images. To evaluate potentials and limitations of ourmethod, we perform experiments on real data. Preliminary results confirm therelationship between splits and merges of clusters and the occurrence ofchanges. However, it becomes evident that it is necessary to incorporate prior,ancillary, or application-specific information to improve the interpretation ofclustering results and to identify unambiguously the areas of change.

A clustering approach to heterogeneous change detection