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

Due to the more dramatic multi-scale variations and more complicatedforegrounds and backgrounds in optical remote sensing images (RSIs), thesalient object detection (SOD) for optical RSIs becomes a huge challenge.However, different from natural scene images (NSIs), the discussion on theoptical RSI SOD task still remains scarce. In this paper, we propose amulti-scale context network, namely MSCNet, for SOD in optical RSIs.Specifically, a multi-scale context extraction module is adopted to address thescale variation of salient objects by effectively learning multi-scalecontextual information. Meanwhile, in order to accurately detect completesalient objects in complex backgrounds, we design an attention-based pyramidfeature aggregation mechanism for gradually aggregating and refining thesalient regions from the multi-scale context extraction module. Extensiveexperiments on two benchmarks demonstrate that MSCNet achieves competitiveperformance with only 3.26M parameters. The code will be available athttps://github.com/NuaaYH/MSCNet.

A lightweight multi-scale context network for salient object detection in optical remote sensing images