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

In this letter, a novel method for change detection is proposed usingneighborhood structure correlation. Because structure features are insensitiveto the intensity differences between bi-temporal images, we perform thecorrelation analysis on structure features rather than intensity information.First, we extract the structure feature maps by using multi-orientated gradientinformation. Then, the structure feature maps are used to obtain theNeighborhood Structural Correlation Image (NSCI), which can represent thecontext structure information. In addition, we introduce a measure namedmatching error which can be used to improve neighborhood information.Subsequently, a change detection model based on the random forest isconstructed. The NSCI feature and matching error are used as the model inputsfor training and prediction. Finally, the decision tree voting is used toproduce the change detection result. To evaluate the performance of theproposed method, it was compared with three state-of-the-art change detectionmethods. The experimental results on two datasets demonstrated theeffectiveness and robustness of the proposed method.

Exploiting Neighborhood Structural Features for Change Detection