Frequency-Aware Integrity Learning Network for Semantic Segmentation of Remote Sensing Images
The semantic segmentation of remote sensing images is crucial for computer perception tasks. Integrating dual-modal information enhances semantic understanding. However, existing segmentation methods often suffer from incomplete feature information (features without integrity), leading to inadequate...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10819987/ |
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| Summary: | The semantic segmentation of remote sensing images is crucial for computer perception tasks. Integrating dual-modal information enhances semantic understanding. However, existing segmentation methods often suffer from incomplete feature information (features without integrity), leading to inadequate segmentation of pixels near object boundaries. This study introduces the concept of integrity in semantic segmentation and presents a complete integrity learning network using contextual semantics in the multiscale feature decoding process. Specifically, we propose a frequency-aware integrity learning network (FILNet) that compensates for missing features by capturing a shared integrity feature, enabling accurate differentiation between object categories and precise pixel segmentation. First, we design a frequency-driven awareness generator that produces an awareness map by extracting frequency-domain features with high-level semantics, guiding the multiscale feature aggregation process. Second, we implement a split–fuse–replenish strategy, which divides features into two branches for feature extraction and information replenishment, followed by cross-modal fusion and direct connection for information replenishment, resulting in fused features. Finally, we present an integrity assignment and enhancement method that leverages a capsule network to learn the correlation of multiscale features, generating a shared integrity feature. This feature is assigned to multiscale features to enhance their integrity, leading to accurate predictions facilitated by an adaptive large kernel module. Experiments on the Vaihingen and Potsdam datasets demonstrate that our method outperforms current state-of-the-art segmentation techniques. |
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| ISSN: | 1939-1404 2151-1535 |