CG-FCLNet:Category-Guided Feature Collaborative Learning Network for Semantic Segmentation of Remote Sensing Images  

作　　者：Min Yao Guangjie Hu Yaozu Zhang 

机构地区：[1]College of Information Engineering,Shanghai Maritime University,Shanghai,201306,China [2]R&D Department,Shanghai Freesense Technology Co.Ltd.,Shanghai,200000,China

出　　处：《Computers, Materials & Continua》2025年第5期2751-2771,共21页计算机、材料和连续体(英文)

基　　金：funded by National Natural Science Foundation of China(61603245).

摘　　要：Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.

关 键 词：Semantic segmentation remote sensing feature context interaction attentionmodule category-guided module 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]