MFG-SCDNet:多尺度感知与变化引导融合的水体语义变化检测网络
MFG-SCDNet:multi-scale awareness and change-guided fusion network for semantic water body change detection
摘要
[目的]针对传统变化检测方法难以区分水体细微变化与语义类别的问题,本文设计了一种能精准识别变化区域并输出语义类别的深度学习模型.[方法]设计多尺度特征引导的语义变化检测网络(MFG-SCDNet),采用双流编码器-解码器架构,结合分组多尺度上下文聚合(GMCA)模块、自适应特征选择融合(AFSF)模块与变化感知注意力模块(CAAM),实现多尺度特征提取与变化区域语义分类.[结果]在SECOND和Landsat-SCD数据集上的试验表明,MFG-SCDNet各项指标优于主流方法,在SECOND上mIoU为73.63%、SeK为23.04%、OA为87.73%;在Landsat-SCD上mIoU为88.98%、SeK为59.34%、OA为96.14%.消融试验验证了各模块的有效性.[结论]MFG-SCDNet能有效提升水体语义变化检测的精度与一致性,为水资源遥感监测提供可靠技术手段.
[Purposes]Aiming at the problem that traditional change detection methods struggle to distinguish subtle water body changes from semantic categories,this study proposes a deep learning model capable of accurately identifying change regions and outputting their semantic categories.[Methods]A multi-scale feature-guided semantic change detection network(MFG-SCDNet)is designed,which adopts a dual-stream encoder-decoder architecture and integrates a grouped multi-scale aggregation(GMCA)module,an adaptive feature selection(AFSF)module,and a change-aware attention module(CAAM)to achieve multi-scale feature extraction and semantic classification of change regions.[Findings]Experiments on the SECOND and Landsat-SCD datasets show that MFG-SCDNet outperforms mainstream methods across all evaluation metrics.On the SECOND dataset,it achieves an mIoU of 73.63%,a SeK of 23.04%,and an OA of 87.73%.On the Landsat-SCD dataset,it achieves an mIoU of 88.98%,a SeK of 59.34%,and an OA of 96.14%.Ablation experiments verify the effectiveness of each module.[Conclusions]MFG-SCDNet can effectively improve the accuracy and consistency of water semantic change detection,providing a reliable technical means for water resource remote sensing monitoring.
年，卷(期)：
2026, (9)
分类号：
P237
机标分类号：
TP391.41
TU248.1
TU986
在线出版日期：
2026年10月28日
基金项目：
2025028:江苏省水利科技项目42401456:国家自然科学基金2408085QD123:安徽省自然科学基金青年项目，
