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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Change detection is one of the main driving forces for the continuous development of remote sensing image processing technology. As a pixel-level detection task, change detection attaches great importance to the utilization of original spatial information as well as contextual information. In this paper, we propose a deep learning-based change detection method that combines full-scale skip connections and mixed pooling module (MPM). Firstly, full-scale skip connections make full use of the spatial information of features at different scales to improve change detection performance. In addition, we introduce MPM in this method due to the complexity of scene information in images, which can enhance the understanding of the scene with rich contextual information and boost the detection ability of strip shape targets. Finally, our experimental results on two public datasets demonstrate that the proposed method has good performance.

A Change Detection Method Based on Full-scale Skip Connections and Mixed Pooling Module