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.
View on arXiv@article{wang2025_2504.16564,
title={ SAIP-Net: Enhancing Remote Sensing Image Segmentation via Spectral Adaptive Information Propagation },
author={ Zhongtao Wang and Xizhe Cao and Yisong Chen and Guoping Wang },
journal={arXiv preprint arXiv:2504.16564},
year={ 2025 }
}