自然资源遥感2025，Vol.37Issue(2)：11-18,8.DOI:10.6046/zrzyyg.2023302
基于样本迭代优化策略的密集连接多尺度土地覆盖语义分割
Densely connected multiscale semantic segmentation for land cover based on the iterative optimization strategy for samples
摘要
Abstract
To address the issues of missing small-scale surface features and incomplete continuous features in segmentation results,this study proposed a densely connected multiscale semantic segmentation network(DMS-Net)model for land cover segmentation.The model integrates a multiscale densely connected atrous spatial convolution pyramid pooling module and strip pooling to extract multiscale and spatially continuous features.A position paralleling Channel attention module(PPCA)is employed to assess feature weights for high-efficiency expression.A cascade low-level feature fusion(CLFF)module is applied to capture neglected low-level features,further complementing details.Experimental results demonstrate that the DMS-Net model achieved an overall accuracy(OA)of 89.97％ and a mean intersection over union(mIoU)of 75.59％ on an iteratively extended dataset,outperforming traditional machine learning methods and deep learning models like U-Net,PSPNet,and Deeplabv3+.The segmentation results of the DMS-Net model reveal structurally complete surface features with clear boundaries,underscoring its practical value in multiscale extraction and analysis of remote sensing information for land cover.关键词
深度学习/全卷积神经网络/多尺度/语义分割/土地覆盖Key words
deep learning/fully convolutional neural network/multiscale/semantic segmentation/land cover分类
计算机与自动化引用本文复制引用
郑宗生,高萌,周文睆,王政翰,霍志俊,张月维..基于样本迭代优化策略的密集连接多尺度土地覆盖语义分割[J].自然资源遥感,2025,37(2):11-18,8.基金项目
国家自然科学基金项目"一种面向多模态遥感信息的质量抽样检验方案研究"(编号:41671431)、上海市科委地方能力建设项目"复杂潮汐环境下海岛(礁)地物信息提取与精度验证方法及其示范应用"(编号:19050502100)和广州气象卫星地面站项目"基于气象卫星遥感的台风中心定位AI模型引进"(编号:D-8006-23-0157)共同资助. （编号:41671431）
