A Unified Framework with Dynamic Kernel Learning for Bidirectional Feature Resampling in Remote Sensing Images.
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| Title: | A Unified Framework with Dynamic Kernel Learning for Bidirectional Feature Resampling in Remote Sensing Images. |
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| Authors: | Xiang, Jiajun1 (AUTHOR), Xiao, Zixuan1 (AUTHOR), Wang, Shuojie1 (AUTHOR), Fu, Ruigang1 (AUTHOR) furuigang08@nudt.edu.cn, Zhong, Ping1 (AUTHOR) |
| Source: | Remote Sensing. Nov2025, Vol. 17 Issue 21, p3599. 21p. |
| Subjects: | Remote-sensing images, Resampling (Statistics), Parameterization, Object recognition (Computer vision), Interpolation, Feature extraction |
| Abstract: | Highlights: What are the main findings? A lightweight dynamic resampling kernel based on a compact source space and bilinear interpolation achieves competitive performance with significantly fewer parameters, eliminating the need for learnable offsets and channel compression. A unified bidirectional resampling framework ensures architectural consistency across upsampling and downsampling operations, enhancing multiscale feature learning for remote sensing object detection. Extensive validation on DIOR and DOTA benchmarks shows consistent performance improvements over baseline methods under substantially reduced parameter constraints. What is the implication of the main finding? The method provides a highly parameter-efficient alternative to existing learnable resamplers, making it suitable for resource-constrained deployment scenarios. The unified design facilitates more coherent feature representation learning across scales, which is essential for accurate detection in complex remote sensing imagery. The inherent multiscale nature of objects poses a fundamental challenge in remote sensing object detection. To address this, feature pyramids have been widely adopted as a key architectural component. However, the effectiveness of these pyramids critically depends on the sampling operations used to construct them, highlighting the need to move beyond traditional fixed-kernel methods. While conventional interpolation approaches (e.g., nearest-neighbor and bilinear) are computationally efficient, their content-agnostic nature often leads to detail loss and artifacts. Recent dynamic sampling operators improve performance through content-aware mechanisms, yet they typically incur substantial computational and parametric costs, hindering their applicability in resource-constrained scenarios. To overcome these limitations, we propose Lurker, a learned and unified resampling kernel that supports both upsampling and downsampling within a consistent framework. Lurker constructs a compact source kernel space and employs bilinear interpolation to generate adaptive kernels, enabling content-aware feature reassembly while maintaining a lightweight parameter footprint. Extensive experiments on the DIOR and DOTA datasets demonstrate that Lurker achieves a favorable trade-off between detection accuracy and efficiency, outperforming existing resampling methods in terms of both accuracy and parameter efficiency, making it especially suitable for remote sensing object detection applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A lightweight dynamic resampling kernel based on a compact source space and bilinear interpolation achieves competitive performance with significantly fewer parameters, eliminating the need for learnable offsets and channel compression. A unified bidirectional resampling framework ensures architectural consistency across upsampling and downsampling operations, enhancing multiscale feature learning for remote sensing object detection. Extensive validation on DIOR and DOTA benchmarks shows consistent performance improvements over baseline methods under substantially reduced parameter constraints. What is the implication of the main finding? The method provides a highly parameter-efficient alternative to existing learnable resamplers, making it suitable for resource-constrained deployment scenarios. The unified design facilitates more coherent feature representation learning across scales, which is essential for accurate detection in complex remote sensing imagery. The inherent multiscale nature of objects poses a fundamental challenge in remote sensing object detection. To address this, feature pyramids have been widely adopted as a key architectural component. However, the effectiveness of these pyramids critically depends on the sampling operations used to construct them, highlighting the need to move beyond traditional fixed-kernel methods. While conventional interpolation approaches (e.g., nearest-neighbor and bilinear) are computationally efficient, their content-agnostic nature often leads to detail loss and artifacts. Recent dynamic sampling operators improve performance through content-aware mechanisms, yet they typically incur substantial computational and parametric costs, hindering their applicability in resource-constrained scenarios. To overcome these limitations, we propose Lurker, a learned and unified resampling kernel that supports both upsampling and downsampling within a consistent framework. Lurker constructs a compact source kernel space and employs bilinear interpolation to generate adaptive kernels, enabling content-aware feature reassembly while maintaining a lightweight parameter footprint. Extensive experiments on the DIOR and DOTA datasets demonstrate that Lurker achieves a favorable trade-off between detection accuracy and efficiency, outperforming existing resampling methods in terms of both accuracy and parameter efficiency, making it especially suitable for remote sensing object detection applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17213599 |
