Knowledge Commons of Institute of Automation,CAS
GFFNet: Global Feature Fusion Network for Semantic Segmentation of Large-Scale Remote Sensing Images | |
Cao, Yong1,2![]() ![]() ![]() ![]() | |
发表期刊 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
![]() |
ISSN | 1939-1404 |
2024-01 | |
卷号 | 17期号:2024页码:4222 - 4234 |
文章类型 | 国际期刊 |
摘要 | Semantic segmentation plays a pivotal role in interpreting high-resolution remote sensing images (RSIs), where contextual information is essential for achieving accurate segmentation. Despite the common practice of partitioning large RSIs into smaller patches for deep model input, existing methods often rely on adaptations from natural image semantic segmentation techniques, limiting their contextual scope to individual images. To address this limitation and harness a broader range of contextual information from original large-scale RSIs, this study introduces a global feature fusion network (GFFNet). GFFNet employs a novel approach by incorporating a group transformer structure alternated with group convolution, forming a lightweight global context learning branch. This design facilitates the extraction of global contextual features from the large-scale RSIs. In addition, we propose a cross feature fusion module that seamlessly integrates local features obtained from the convolutional network with the global contextual features. GFFNet serves as a versatile plugin for existing RSI semantic segmentation models, particularly beneficial when the target dataset involves cropping. This integration enhances the model's performance, especially in terms of segmenting large-scale objects. Experimental results on the ISPRS and GID-15 datasets validate the effectiveness of GFFNet in improving segmentation capabilities for large scale objects in RSIs. |
关键词 | Cross feature fusion (CFF) global context learning group transformer semantic segmentation |
学科门类 | 工学 |
DOI | 10.1109/JSTARS.2024.3359656 |
URL | 查看原文 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Major Project for New Generation of AI[2018AAA0100400] |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 多尺度信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57559 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Huo, Chunlei |
作者单位 | 1.National Key Laboratory for Multi-Modal Ar tificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Cao, Yong,Huo, Chunlei,Xiang, Shiming,et al. GFFNet: Global Feature Fusion Network for Semantic Segmentation of Large-Scale Remote Sensing Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2024,17(2024):4222 - 4234. |
APA | Cao, Yong,Huo, Chunlei,Xiang, Shiming,&Pan, Chunhong.(2024).GFFNet: Global Feature Fusion Network for Semantic Segmentation of Large-Scale Remote Sensing Images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,17(2024),4222 - 4234. |
MLA | Cao, Yong,et al."GFFNet: Global Feature Fusion Network for Semantic Segmentation of Large-Scale Remote Sensing Images".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17.2024(2024):4222 - 4234. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
GFFNet_Global_Featur(4340KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论