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Enhanced salient object detection in remote sensing images via dual-stream semantic interactive network

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Abstract

Salient object detection in remote sensing images (RSI-SOD) aims to identify the most prominent regions within complex RSI scenes. Current convolutional neural network (CNN)-based approaches struggle to capture long-distance dependencies, limiting their performance. To address this, we propose a novel dual-stream semantic interactive network (DSINet). Specifically, the model combines the advantages of Transformer and CNN to simultaneously model both global relationships and local details via the dual-stream architecture. It comprises three key modules: a multi-scale feature enhancement module to enhance feature representations across scales, a cross-attention complementary mining module to explore complementary cues between Transformer and CNN features, and a cross-layer feature interaction module to mitigate inconsistencies between adjacent layers. Extensive experiments on benchmark datasets demonstrate that DSINet achieves superior performance compared to state-of-the-art methods, effectively identifying salient objects in challenging RSI scenes. The code and results of our method are available at https://github.com/dqxfj99/DSINet.

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Data availability

The datasets can be found in the https://github.com/rmcong/EORSSD-dataset, https://li-chongyi.github.io/proj_optical_saliency.html, and https://github.com/wchao1213/ORSI-SOD.

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Acknowledgements

This paper was supported by the National Natural Science Foundation of China (No. 62471124), Heilongjiang Province Natural Science Foundation (No. LH2022F005), and Young Top Talents Fund in the School of Electrical Information Engineering of Northeast Petroleum University (No. DYDQQB202204).

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Authors and Affiliations

  1. School of Electrical Information Engineering, Northeast Petroleum University, Daqing, 163000, China

    Yanliang Ge, Taichuan Liang, Junchao Ren, Jiaxue Chen & Hongbo Bi

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  1. Yanliang Ge
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  2. Taichuan Liang
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  3. Junchao Ren
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  4. Jiaxue Chen
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Contributions

Yanliang Ge provided software and contributed to validation and writing—original draft. Taichuan Liang was involved in methodology, validation, and writing—original draft, and provided software. Junchao Ren contributed to visualization, writing—review, and validation. Jiaxue Chen was involved in data curation, investigation, and validation. Hongbo Bi contributed to methodology and writing—review.

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Correspondence to Hongbo Bi.

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Ge, Y., Liang, T., Ren, J. et al. Enhanced salient object detection in remote sensing images via dual-stream semantic interactive network. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03713-8

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