PPMamba: Enhancing Semantic Segmentation in Remote Sensing Imagery by SS2D

Juwei Mu;Shangbo Zhou;Xingjie Sun
{"title":"PPMamba: Enhancing Semantic Segmentation in Remote Sensing Imagery by SS2D","authors":"Juwei Mu;Shangbo Zhou;Xingjie Sun","doi":"10.1109/LGRS.2024.3507033","DOIUrl":null,"url":null,"abstract":"Remote sensing semantic segmentation is a critical technology in the field of remote sensing image processing, with broad applications in environmental monitoring, urban planning, disaster assessment, and resource exploration. Despite the transformative impact of convolutional neural networks (CNNs) on this domain, CNN-based methods often encounter limitations due to their localized receptive fields, which struggle to capture the global context necessary for accurate segmentation in complex remote sensing imagery. In this letter, a novel approach is presented for remote sensing semantic segmentation using a mamba-based model named PPmamba. The PPmamba model integrates Resblock and PPmamba within an encoder-decoder framework to effectively capture both local and global contextual information from high-resolution remote sensing images. Leveraging the strengths of the Mamba architecture, our model employs selective scanning to efficiently process long sequences, overcoming the limitations of traditional CNNs and transformers in handling large-scale images with complex scenes. Extensive experiments on two benchmark datasets (Potsdam and Vaihingen) demonstrate the superiority of our PPmamba model against state-of-the-art models, achieving significant improvements in segmentation results. The codes will be available at \n<uri>https://github.com/Jerrymo59/PPMambaSeg</uri>\n.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10769411/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Remote sensing semantic segmentation is a critical technology in the field of remote sensing image processing, with broad applications in environmental monitoring, urban planning, disaster assessment, and resource exploration. Despite the transformative impact of convolutional neural networks (CNNs) on this domain, CNN-based methods often encounter limitations due to their localized receptive fields, which struggle to capture the global context necessary for accurate segmentation in complex remote sensing imagery. In this letter, a novel approach is presented for remote sensing semantic segmentation using a mamba-based model named PPmamba. The PPmamba model integrates Resblock and PPmamba within an encoder-decoder framework to effectively capture both local and global contextual information from high-resolution remote sensing images. Leveraging the strengths of the Mamba architecture, our model employs selective scanning to efficiently process long sequences, overcoming the limitations of traditional CNNs and transformers in handling large-scale images with complex scenes. Extensive experiments on two benchmark datasets (Potsdam and Vaihingen) demonstrate the superiority of our PPmamba model against state-of-the-art models, achieving significant improvements in segmentation results. The codes will be available at https://github.com/Jerrymo59/PPMambaSeg .
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Deeper and Broader Multimodal Fusion: Cascaded Forest-of-Experts for Land Cover Classification Impact of Targeted Sounding Observations From FY-4B GIIRS on Two Super Typhoon Forecasts in 2024 Structural Representation-Guided GAN for Remote Sensing Image Cloud Removal Multispectral Airborne LiDAR Point Cloud Classification With Maximum Entropy Hierarchical Pooling A Satellite Selection Algorithm for GNSS-R InSAR Elevation Deformation Retrieval
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1