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

Lake extraction from remote sensing images is challenging due to the complexlake shapes and inherent data noises. Existing methods suffer from blurredsegmentation boundaries and poor foreground modeling. This paper proposes ahybrid CNN-Transformer architecture, called LEFormer, for accurate lakeextraction. LEFormer contains three main modules: CNN encoder, Transformerencoder, and cross-encoder fusion. The CNN encoder effectively recovers localspatial information and improves fine-scale details. Simultaneously, theTransformer encoder captures long-range dependencies between sequences of anylength, allowing them to obtain global features and context information. Thecross-encoder fusion module integrates the local and global features to improvemask prediction. Experimental results show that LEFormer consistently achievesstate-of-the-art performance and efficiency on the Surface Water and theQinghai-Tibet Plateau Lake datasets. Specifically, LEFormer achieves 90.86% and97.42% mIoU on two datasets with a parameter count of 3.61M, respectively,while being 20 minor than the previous best lake extraction method. The sourcecode is available at https://github.com/BastianChen/LEFormer.

LEFormer: A Hybrid CNN-Transformer Architecture for Accurate Lake Extraction from Remote Sensing Imagery