Phase Congruency-Guided Cross-Scale Contextual Fusion Network for Salient Object Detection in Optical Remote Sensing Images.
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| Title: | Phase Congruency-Guided Cross-Scale Contextual Fusion Network for Salient Object Detection in Optical Remote Sensing Images. |
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| Authors: | Jiang, Junfang1 (AUTHOR), Wang, Wanjin2 (AUTHOR), Lin, Xiaohui3 (AUTHOR), Miao, Pingping3,4 (AUTHOR), Gao, Lina1,4 (AUTHOR), Xu, Mingzhu2,3 (AUTHOR) xumingzhu@sdu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1847. 21p. |
| Subjects: | Optical remote sensing, Attention, Object recognition (Computer vision), Deep learning, Feature extraction |
| Abstract: | Highlights: What are the main findings? The Phase Congruency Enhanced Module (PCE) significantly improves feature representation in low-contrast scenarios by leveraging frequency-domain phase information. The Dynamic Residual Fusion Module (DRF) effectively suppresses complex background interference through dynamic spatial attention and residual connections. What are the implications of the main findings? The successful integration of phase congruency into the deep learning framework provides a robust solution for detecting targets with blurred edges or low contrast against backgrounds. The dynamic spatial attention and residual mechanisms in DRF prove effective in distinguishing salient objects from complex backgrounds. In recent years, salient object detection in optical remote sensing images (ORSI-SOD) has garnered increasing research attention. However, in practical applications, issues such as blurred target edges under low-contrast and complex background interference continue to restrict the accuracy and robustness of detection. To address these problems, this paper proposes the Phase Congruency-Guided Cross-Scale Contextual Fusion Network (PCFNet). Specifically, we design a novel Phase Congruency Enhanced Module (PCE) to solve the problem of low-contrast between targets and backgrounds. It acquire phase features via Fourier decomposition and employs them to generate a weighting map to modulate the shallow features via element-wise multiplication, thereby highlighting structurally significant regions. Meanwhile, we adopt a tailored loss weighting mechanism to weight phase congruency learning for better PCE adaptation. To address complex background interference, we design a novel Dynamic Residual Fusion (DRF) Module. It leverages dynamic spatial attention to generate sample-specific kernels that perform convolution to spatially weight features and uses consecutive residual connection, thereby refining multi-scale features to accurately capture effective targets under complex background interference. Experiments on ORSSD, EORSSD, and ORSI4199 benchmarks demonstrate that PCFNet achieves nine best performances and three second-best performances across the twelve core evaluation metrics, outperforming 23 state-of-the-art methods. Notably, the F β score is 1.16% higher than HFCNet on ORSSD and 0.85% higher than MCPNet on EORSSD. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The Phase Congruency Enhanced Module (PCE) significantly improves feature representation in low-contrast scenarios by leveraging frequency-domain phase information. The Dynamic Residual Fusion Module (DRF) effectively suppresses complex background interference through dynamic spatial attention and residual connections. What are the implications of the main findings? The successful integration of phase congruency into the deep learning framework provides a robust solution for detecting targets with blurred edges or low contrast against backgrounds. The dynamic spatial attention and residual mechanisms in DRF prove effective in distinguishing salient objects from complex backgrounds. In recent years, salient object detection in optical remote sensing images (ORSI-SOD) has garnered increasing research attention. However, in practical applications, issues such as blurred target edges under low-contrast and complex background interference continue to restrict the accuracy and robustness of detection. To address these problems, this paper proposes the Phase Congruency-Guided Cross-Scale Contextual Fusion Network (PCFNet). Specifically, we design a novel Phase Congruency Enhanced Module (PCE) to solve the problem of low-contrast between targets and backgrounds. It acquire phase features via Fourier decomposition and employs them to generate a weighting map to modulate the shallow features via element-wise multiplication, thereby highlighting structurally significant regions. Meanwhile, we adopt a tailored loss weighting mechanism to weight phase congruency learning for better PCE adaptation. To address complex background interference, we design a novel Dynamic Residual Fusion (DRF) Module. It leverages dynamic spatial attention to generate sample-specific kernels that perform convolution to spatially weight features and uses consecutive residual connection, thereby refining multi-scale features to accurately capture effective targets under complex background interference. Experiments on ORSSD, EORSSD, and ORSI4199 benchmarks demonstrate that PCFNet achieves nine best performances and three second-best performances across the twelve core evaluation metrics, outperforming 23 state-of-the-art methods. Notably, the F β score is 1.16% higher than HFCNet on ORSSD and 0.85% higher than MCPNet on EORSSD. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111847 |
