Article(id=1311360883691971182, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763049600000, receivedDateStr=2025-11-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582398578, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582398578, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582398578, creator=13701087609, updateTime=1790582398578, updator=13701087609, issue=Issue{id=1311360859318870426, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='4', pageStart='1', pageEnd='185', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790582392768, creator='13701087609', updateTime=1790583829226, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311366884331716830, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311366884331716831, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=164, endPage=174, ext={EN=ArticleExt(id=1311360883939435119, articleId=1311360883691971182, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=A residual-state space model hybrid architecture for arctic sea ice segmentation, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=

To address the challenge of simultaneously extracting local and global features in multi-task segmentation of Arctic sea ice, a hybrid architecture integrating residual networks and state space models was proposed in this paper. In this architecture, residual networks were employed as the encoder to extract multi-scale local features. Enhanced Visual State Space (EVSS) modules were adopted in the decoder to achieve global context modeling through the combination of 2D Selective Scan and dual attention mechanisms. In the skip connections, Cross-Scale Contextual Fusion (CSCF) modules were designed to integrate multi-level features from the encoder. Attention mechanisms were utilized to enhance the representation of key regions. In the training phase, a Decoder Stage-Wise Supervision (DSWS) was introduced to optimize the calculation of loss functions. Finally, sea ice concentration, development stages, and floe size predictions were generated simultaneously through parallel segmentation heads. Experiments on the AI4Arctic dataset demonstrate that the proposed model achieves a comprehensive score of 84.014. Its performance exceeds that of mainstream methods such as Swin Transformer and SegFormer in all three prediction tasks. Fine-grained segmentation of sea ice distribution is achieved in this paper. The results provide technical support for subsequent identification and delimitation of navigable water areas. The obtained segmentation results hold significant application value in Arctic route planning and ship navigation safety assessment.

, authors=Jicheng Yu, Jiaming Qu^*, Yuchi Hao, authorsList=Jicheng Yu, Jiaming Qu, Yuchi Hao, authorCompany=null, correspAuthors=Jiaming Qu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311360884274979440, articleId=1311360883691971182, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于残差-状态空间模型混合架构的北极海冰分割网络, columnId=1240655018068595518, journalTitle=中国航海, columnName=海上交通安全, runingTitle=null, highlight=null, articleAbstract=

针对北极海冰多任务分割中局部与全局特征提取难以兼顾的挑战，提出一种融合了残差网络与状态空间模型的混合架构。该架构以残差网络为编码器提取多尺度局部特征，解码器则采用增强型视觉状态空间模块，通过结合二维选择性扫描与双重注意力机制实现全局的上下文建模；在跳跃连接部分，设计了跨尺度上下文融合模块以整合编码器多层级特征，利用注意力机制强化关键区域表达；在训练阶段，引入了解码器分级监督策略优化损失函数计算；最后通过并行分割头同步输出海冰密集度、发展阶段及浮冰尺寸预测。在AI4Arctic数据集上的试验表明，所提模型综合评分达84.014，在所有的3种预测任务上的表现均优于Swin Transformer、SegFormer等主流方法，实现了海冰分布的精细分割，其成果可为后续识别并划定可通航水域范围提供技术支撑。所获分割结果在北极航道规划与船舶航行安全评估中具有重要的应用价值。

, authors=郁纪成, 曲嘉铭^*, 郝宇驰, authorsList=郁纪成, 曲嘉铭, 郝宇驰, authorCompany=null, correspAuthors=曲嘉铭, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=t+FSf266/VLcfvbh+KFC7g==, magXml=PRNqTCjpR8OHcwye43nduA==, pdfUrl=null, pdf=l2kHbB6Uj+PZrphLlKBVVw==, pdfFileSize=2244660, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ibVLqm8sJBGzRpmMksMdIg==, mapNumber=null, fund=null)}, authors=[Author(id=1311360884543414901, tenantId=1146029695717560320, journalId=1240618002186551303, articleId=1311360883691971182, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=yujicheng@ccccltd.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1311360884627300983, tenantId=1146029695717560320, journalId=1240618002186551303, articleId=1311360883691971182, authorId=1311360884543414901, language=EN, stringName=Jicheng Yu, firstName=Jicheng, middleName=null, lastName=Yu, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=CCCC National Engineering Research Center of Dredging Technology and Equipment Co., Ltd., Shanghai 200082, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1311360884698604152, tenantId=1146029695717560320, journalId=1240618002186551303, articleId=1311360883691971182, authorId=1311360884543414901, language=CN, stringName=郁纪成, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=中交疏浚技术装备国家工程研究中心有限公司，上海 200082, bio={"content":"

郁纪成（2000—），男，助理工程师，研究方向为深度学习与海洋遥感，（E-mail）yujicheng@ccccltd.cn

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郁纪成（2000—），男，助理工程师，研究方向为深度学习与海洋遥感，（E-mail）yujicheng@ccccltd.cn

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