通过频谱自适应传播提升遥感图像分割精度
SAIP-Net: Enhancing Remote Sensing Image Segmentation via Spectral Adaptive Information Propagation
- 引入频谱自适应信息传播机制，动态优化特征融合
- 在多个遥感数据集上显著提升边界清晰度与类内一致性
- 适合关注遥感图像细粒度分割的研究者和应用开发者
遥感图像语义分割需兼顾精确的空间边界与强类内一致性，传统层级模型在空间域特征融合和感受野不足方面存在局限。本文提出SAIP-Net，一种新型频谱感知分割框架，利用频谱自适应信息传播机制。该方法通过自适应频域滤波与多尺度感受野增强，有效抑制类内特征不一致并锐化边界。大量实验表明，其性能显著优于现有先进方法，验证了频谱自适应策略结合扩展感受野在遥感图像分割中的有效性。
原文摘要 · Abstract (English)
Semantic segmentation of remote sensing imagery demands precise spatial boundaries and robust intra-class consistency, challenging conventional hierarchical models. To address limitations arising from spatial domain feature fusion and insufficient receptive fields, this paper introduces SAIP-Net, a novel frequency-aware segmentation framework that leverages Spectral Adaptive Information Propagation. SAIP-Net employs adaptive frequency filtering and multi-scale receptive field enhancement to effectively suppress intra-class feature inconsistencies and sharpen boundary lines. Comprehensive experiments demonstrate significant performance improvements over state-of-the-art methods, highlighting the effectiveness of spectral-adaptive strategies combined with expanded receptive fields for remote sensing image segmentation.
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