RRNet : Relational Reasoning Network with Parallel Multiscale Attention for Salient Object Detection in Optical Remote Sensing Images

Runmin CONG, Yumo ZHANG, Leyuan FANG, Jun LI, Yao ZHAO, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

87 Citations (Scopus)

Abstract

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Since some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network (RRNet) with parallel multiscale attention (PMA) for SOD in optical RSIs in this article. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The PMA module is proposed to effectively restore the detailed information and address the scale variation of salient objects by using the low-level features refined by multiscale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively ( https://rmcong.github.io/proj_RRNet.html ).
Original languageEnglish
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
Early online date28 Oct 2021
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Optical remote sensing images
  • parallel multiscale attention
  • relational reasoning
  • salient object detection

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