# SAIP-Net: Enhancing Remote Sensing Image Segmentation via Spectral Adaptive Information Propagation

Source: [SAIP-Net: Enhancing Remote Sensing Image Segmentation via Spectral Adaptive Information Propagation](<https://www.sota2.com/research/paper/68fa7c241e209131cea8ca7b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T07:04:32.931Z

Authors: Zhongtao Wang, Xizhe Cao, Yisong Chen, Guoping Wang

Published: 2025-04-23T09:43:58.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.16564>)

[PDF](<https://arxiv.org/pdf/2504.16564.pdf>)