Brain-Inspired Synergistic Adversarial Framework for Style Transfer-Guided Semantic Segmentation in Cross-Domain Remote Sensing Imagery
Domain shifts pose significant challenges for cross-domain semantic segmentation in high-resolution remote sensing imagery. Inspired by the cognitive mechanisms of the human brain, we propose a Brain-Inspired Style Transfer and Semantic Segmentation Collaborative Adversarial Framework (SAF), which m...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/11/1834 |
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| Summary: | Domain shifts pose significant challenges for cross-domain semantic segmentation in high-resolution remote sensing imagery. Inspired by the cognitive mechanisms of the human brain, we propose a Brain-Inspired Style Transfer and Semantic Segmentation Collaborative Adversarial Framework (SAF), which mimics neural processes such as hierarchical perception, memory retrieval, and multimodal integration to enhance cross-domain feature alignment and segmentation performance. To achieve the joint optimization of style transfer and semantic segmentation networks, we introduce three key components: a Semantic-Aware Transformer Module (SATM) that dynamically captures and preserves essential semantic features during style transfer; a Semantic-Driven Multi-feature Memory Module (SMM) that stores and retrieves historical style and semantic information to improve domain adaptability; a Domain-Invariant Style-Semantic Center Space (DSCS) that aligns style and semantic features within a shared representation space, mitigating discrepancies between style and semantic domains. Extensive experiments across multiple tasks demonstrate that SAF effectively reduces distortions and semantic inconsistencies by achieving deep style–semantic alignment. Compared to leading approaches, SAF achieves a superior balance between style adaptation and semantic preservation, significantly improving model generalization in remote sensing applications. |
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| ISSN: | 2072-4292 |