信号处理2025，Vol.41Issue(8)：1323-1331,9.DOI:10.12466/xhcl.2025.08.002
粗粒度类别信息引导的精细化遥感地物分类
Coarse-Grained Category Information-Guided Fine-Grained Remote Sensing Land Cover Classification
摘要
Abstract
Land cover classification is an important task for remote sensing image interpretation that is employed for de-tailed terrain and landform analysis and plays a critical role in urban planning,disaster monitoring,and other fields.Synthetic aperture radar(SAR)images are widely used in land cover classification owing to their all-day,all-weather capability,high resolution,and large coverage area.However,SAR images also have limitations:they contain numer-ous shadows and significant noise due to their unique imaging mechanism.High-resolution SAR images are character-ized by complex land cover distributions,large scale variations among targets,and lack of color information.This can easily cause boundary blurring and category confusion.In this paper,we propose a refined land cover classification method guided by coarse-grained category information.This method leverages multi-scale semantic features and coarse category information guidance to enhance the effective semantic feature extraction ability,category discrimination abil-ity,and segmentation performance of a model.To address the issue of numerous shadows and significant noise in SAR images,we propose to employ a residual neural network(ResNet50)for multi-scale feature extraction.The extracted features are processed through a skip-connected decoder combined with an atrous spatial pyramid pooling(ASPP)mod-ule,which enhances context modeling capabilities,further preserves edge and structural information,and improves ro-bustness against noise interference.To address the problem of unclear boundaries and category confusion in SAR im-ages,we designed a coarse-grained category guidance module.This module generates dynamic semantic prototypes us-ing class and semantic space features and then weights semantic features to enhance inter-category discrimination.Ex-periments on the WHU-OPT-SAR dataset show that our algorithm exhibits better semantic structure discrimination in complex scenes while preserving edge clarity.It particularly boosts segmentation accuracy for easily confused categories such as roads and waters.关键词
合成孔径雷达/地物分类/多尺度特征/语义原型Key words
synthetic aperture radar/land cover classification/multi-scale features/semantic prototype分类
信息技术与安全科学引用本文复制引用
陈禾,郭华哲,董珊,庄胤..粗粒度类别信息引导的精细化遥感地物分类[J].信号处理,2025,41(8):1323-1331,9.基金项目
国家自然科学基金委员会叶企孙科学基金(U2341202)Ye Qisun Science Foundation of the National Natural Science Foundation of China(U2341202) （U2341202）
