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

Change detection (CD) is an essential task for various real-worldapplications, such as urban management and disaster assessment. However,previous methods primarily focus on improving the accuracy of CD, whileneglecting the reliability of detection results. In this paper, we propose anovel change detection network, called AR-CDNet, which is able to provideaccurate change maps and generate pixel-wise uncertainty. Specifically, anonline uncertainty estimation branch is constructed to model the pixel-wiseuncertainty, which is supervised by the difference between predicted changemaps and corresponding ground truth during the training process. Furthermore,we introduce a knowledge review strategy to distill temporal change knowledgefrom low-level features to high-level ones, thereby enhancing thediscriminability of temporal difference features. Finally, we aggregate theuncertainty-aware features extracted from the online uncertainty estimationbranch with multi-level temporal difference features to improve the accuracy ofCD. Once trained, our AR-CDNet can provide accurate change maps and evaluatepixel-wise uncertainty without ground truth. Experimental results on twobenchmark datasets demonstrate the superior performance of AR-CDNet in the CDtask. The demo code for our work will be publicly available at\url{https://github.com/guanyuezhen/AR-CDNet}.

Towards Accurate and Reliable Change Detection of Remote Sensing Images via Knowledge Review and Online Uncertainty Estimation