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 .
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信