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Article(id=1322560028796629604, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1746979200000, receivedDateStr=2025-05-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252482841, onlineDateStr=2026-10-29, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252482841, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252482841, creator=13701087609, updateTime=1793252482841, updator=13701087609, issue=Issue{id=1322559951067792299, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='4', pageStart='1', pageEnd='294', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252464310, creator='13701087609', updateTime=1793252763065, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322561204200001631, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322561204200001632, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=214, endPage=221, ext={EN=ArticleExt(id=1322560028972790373, articleId=1322560028796629604, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Study on Precise Identification of Mine Surface Features Based on the Dual-Branch DB-TransUNet Model, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issue of insufficient surface feature recognition accuracy in traditional segmentation models caused by complex vegetation cover and blurred surface feature boundaries in mining remote sensing images, a dual-branch DB-TransUNet model was proposed. This model innovatively constructed a dual-branch encoding architecture and integrated features from both branches using an adaptive weighted fusion (AWF) module. By combining this with a channel adaptive enhancement (CA) module to optimize feature weight allocation during the decoding process, it achieved focused detection of features in key regions and effective suppression of background noise. The experimental results show that incorporating the AWF and CA modules improved the mIoU of the model by 4.16% and 78.63%, respectively. The full DB-TransUNet model achieves excellent segmentation results of mIoU=0.903 7 and mDice=0.948 8 in complex remote sensing scenarios. Compared to mainstream network segmentation architectures, the DB-TransUNet model demonstrates superior performance across all indicators, achieving higher accuracy and stronger generalization capabilities in the recognition and detailed segmentation of complex surface features in mining environments. The findings validates the effectiveness of a strategy combining complementary multi-source features with weighted fusion in mitigating vegetation-induced interference in the identification of mine surface features, thereby providing a reliable technical solution for high-precision intelligent monitoring in open-pit mining areas.

, authors=Qi YAO1, 2, Shaodan LI1, 2, 3, Pengshuai XUE2, 3, Xiaolong ZHU1, 4, Yunxu YUAN1, 4, Yincui HU1, 2, 3, authorsList=Qi YAO, Shaodan LI, Pengshuai XUE, Xiaolong ZHU, Yunxu YUAN, Yincui HU, authorCompany=null, correspAuthors=Shaodan LI, 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=1322560029681627750, articleId=1322560028796629604, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于双分支DB-TransUNet模型的矿山地物精准识别研究, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对矿山遥感影像中植被覆盖复杂、地物边界模糊导致传统分割模型地物识别精度不足的问题，提出了一种双分支DB-TransUNet模型。该模型创新性地构建了双分支编码结构，并通过自适应加权融合（AWF）模块整合双分支特征，结合通道自适应增强（CA）模块优化解码过程中的特征权重分配，从而实现了关键区域特征的聚焦与背景噪声的有效抑制。试验结果表明，引入AWF与CA模块，模型的mIoU分别提升了4.16%和78.63%；完整DB-TransUNet模型在复杂遥感场景中取得mIoU=0.903 7、mDice=0.948 8的优异分割效果；对比主流网络分割架构，DB-TransUNet模型各项指标更为优异，在矿山复杂地物的识别与精细分割上精度更高、泛化能力更强。研究验证了多源特征互补与权重融合策略在缓解矿山地物识别受植被干扰问题方面的有效性，为露天矿区的高精度智能监测提供了可靠的技术方案。

, authors=姚奇1, 2, 李少丹1, 2, 3, 薛彭帅2, 3, 朱小龙1, 4, 袁运许1, 4, 胡引翠1, 2, 3, authorsList=姚奇, 李少丹, 薛彭帅, 朱小龙, 袁运许, 胡引翠, authorCompany=null, correspAuthors=李少丹, authorNote=

姚奇（2001—），男，黑龙江绥化人，硕士研究生，主要研究方向为遥感与矿山应用。E-mail:

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李少丹（1989—），女，河北衡水人，博士，副教授，主要研究方向为遥感与矿山应用。E-mail:
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姚奇（2001—），男，黑龙江绥化人，硕士研究生，主要研究方向为遥感与矿山应用。E-mail:

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姚奇（2001—），男，黑龙江绥化人，硕士研究生，主要研究方向为遥感与矿山应用。E-mail:

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基于双分支DB-TransUNet模型的矿山地物精准识别研究
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姚奇 1, 2 , 李少丹 1, 2, 3 , 薛彭帅 2, 3 , 朱小龙 1, 4 , 袁运许 1, 4 , 胡引翠 1, 2, 3
矿业研究与开发 | 矿山机电与矿业智能化 2026,46(4): 214-221
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矿业研究与开发 |矿山机电与矿业智能化 2026 , 46 (4) : 214 -221
基于双分支DB-TransUNet模型的矿山地物精准识别研究
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姚奇（2001—），男，黑龙江绥化人，硕士研究生，主要研究方向为遥感与矿山应用。E-mail:

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2.School of Geographical Sciences, Hebei Normal University, Shijiazhuang, Hebei 050024, China
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2．河北师范大学 地理科学学院，河北 石家庄　050024
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姚奇1, 2 , 李少丹1, 2, 3 , 薛彭帅2, 3, 朱小龙1, 4, 袁运许1, 4, 胡引翠1, 2, 3
作者信息
  • 1．河北省地质资源环境监测与保护重点实验室，河北 石家庄　050022
  • 2．河北师范大学 地理科学学院，河北 石家庄　050024
  • 3．河北省环境变化遥感识别技术创新中心，河北 石家庄　050024
  • 4．河北省地质环境监测院，河北 石家庄　050022
通讯作者:
李少丹（1989—），女，河北衡水人，博士，副教授，主要研究方向为遥感与矿山应用。E-mail:
作者简介:

姚奇（2001—），男，黑龙江绥化人，硕士研究生，主要研究方向为遥感与矿山应用。E-mail:

Study on Precise Identification of Mine Surface Features Based on the Dual-Branch DB-TransUNet Model
Qi YAO1, 2 , Shaodan LI1, 2, 3 , Pengshuai XUE2, 3, Xiaolong ZHU1, 4, Yunxu YUAN1, 4, Yincui HU1, 2, 3
Affiliations
  • 1.Hebei Key Laboratory of Geological Resources, Environment Monitoring and Protection, Shijiazhuang, Hebei 050022, China
  • 2.School of Geographical Sciences, Hebei Normal University, Shijiazhuang, Hebei 050024, China
  • 3.Hebei Technological Innovation Center for Remote Sensing Identification of Environmental Changes, Shijiazhuang, Hebei 050024, China
  • 4.Hebei Institute of Geological Environment Monitoring, Shijiazhuang, Hebei 050022, China
出版时间: 2026-04-25
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针对矿山遥感影像中植被覆盖复杂、地物边界模糊导致传统分割模型地物识别精度不足的问题，提出了一种双分支DB-TransUNet模型。该模型创新性地构建了双分支编码结构，并通过自适应加权融合（AWF）模块整合双分支特征，结合通道自适应增强（CA）模块优化解码过程中的特征权重分配，从而实现了关键区域特征的聚焦与背景噪声的有效抑制。试验结果表明，引入AWF与CA模块，模型的mIoU分别提升了4.16%和78.63%；完整DB-TransUNet模型在复杂遥感场景中取得mIoU=0.903 7、mDice=0.948 8的优异分割效果；对比主流网络分割架构，DB-TransUNet模型各项指标更为优异，在矿山复杂地物的识别与精细分割上精度更高、泛化能力更强。研究验证了多源特征互补与权重融合策略在缓解矿山地物识别受植被干扰问题方面的有效性，为露天矿区的高精度智能监测提供了可靠的技术方案。

矿山遥感影像  /  地物识别  /  特征融合  /  双分支网络  /  植被干扰

To address the issue of insufficient surface feature recognition accuracy in traditional segmentation models caused by complex vegetation cover and blurred surface feature boundaries in mining remote sensing images, a dual-branch DB-TransUNet model was proposed. This model innovatively constructed a dual-branch encoding architecture and integrated features from both branches using an adaptive weighted fusion (AWF) module. By combining this with a channel adaptive enhancement (CA) module to optimize feature weight allocation during the decoding process, it achieved focused detection of features in key regions and effective suppression of background noise. The experimental results show that incorporating the AWF and CA modules improved the mIoU of the model by 4.16% and 78.63%, respectively. The full DB-TransUNet model achieves excellent segmentation results of mIoU=0.903 7 and mDice=0.948 8 in complex remote sensing scenarios. Compared to mainstream network segmentation architectures, the DB-TransUNet model demonstrates superior performance across all indicators, achieving higher accuracy and stronger generalization capabilities in the recognition and detailed segmentation of complex surface features in mining environments. The findings validates the effectiveness of a strategy combining complementary multi-source features with weighted fusion in mitigating vegetation-induced interference in the identification of mine surface features, thereby providing a reliable technical solution for high-precision intelligent monitoring in open-pit mining areas.

Mining remote sensing image  /  Surface feature recognition  /  Feature fusion  /  Dual-branch network  /  Vegetation interference
姚奇, 李少丹, 薛彭帅, 朱小龙, 袁运许, 胡引翠. 基于双分支DB-TransUNet模型的矿山地物精准识别研究. 矿业研究与开发, 2026 , 46 (4) : 214 -221 .
Qi YAO, Shaodan LI, Pengshuai XUE, Xiaolong ZHU, Yunxu YUAN, Yincui HU. Study on Precise Identification of Mine Surface Features Based on the Dual-Branch DB-TransUNet Model[J]. Mining Research and Development, 2026 , 46 (4) : 214 -221 .
基金
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  • 自然资源部部省合作项目(2023ZRBSHZ006)
  • 河北省地质资源环境监测与保护重点实验室开放课题(JCYKP202310)
  • 河北省自然资源厅2025年度科技项目
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  • 接收时间：2025-05-12
  • 首发时间：2026-10-29
  • 出版时间：2026-04-25
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  • 收稿日期：2025-05-12
基金
自然资源部部省合作项目(2023ZRBSHZ006)
河北省地质资源环境监测与保护重点实验室开放课题(JCYKP202310)
河北省自然资源厅2025年度科技项目
作者信息
    1．河北省地质资源环境监测与保护重点实验室，河北 石家庄　050022
    2．河北师范大学 地理科学学院，河北 石家庄　050024
    3．河北省环境变化遥感识别技术创新中心，河北 石家庄　050024
    4．河北省地质环境监测院，河北 石家庄　050022

通讯作者:

李少丹（1989—），女，河北衡水人，博士，副教授，主要研究方向为遥感与矿山应用。E-mail:
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