Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in...

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Vydáno v:	Remote sensing of environment Ročník 299; s. 113856
Hlavní autoři:	Hong, Danfeng, Zhang, Bing, Li, Hao, Li, Yuxuan, Yao, Jing, Li, Chenyu, Werner, Martin, Chanussot, Jocelyn, Zipf, Alexander, Zhu, Xiao Xiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 15.12.2023
Témata:	artificial intelligence China class Cross-city data collection Deep learning Dice loss domain Domain adaptation environment Germany High-resolution network Land cover Multimodal benchmark datasets Remote sensing Segmentation topology China Germany Deep learning High-resolution network Segmentation Multimodal benchmark datasets Domain adaptation Dice loss Land cover Remote sensing Cross-city
ISSN:	0034-4257, 1879-0704
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in the case studies across cities or regions, due to the lack of diverse RS information and cutting-edge solutions with high generalization ability. To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset), which consists of two cross-city scenes, i.e., Berlin-Augsburg (in Germany) and Beijing-Wuhan (in China). Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN for short, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion but also closing the gap derived from enormous differences of RS image representations between different cities by means of adversarial learning. In addition, the Dice loss is considered in HighDAN to alleviate the class imbalance issue caused by factors across cities. Extensive experiments conducted on the C2Seg dataset show the superiority of our HighDAN in terms of segmentation performance and generalization ability, compared to state-of-the-art competitors. The C2Seg dataset and the semantic segmentation toolbox (involving the proposed HighDAN) will be available publicly at https://github.com/danfenghong/RSE_Cross-city. •A new multimodal remote sensing benchmark for cross-city semantic segmentation.•Propose a high-resolution domain adaptation network for semantic segmentation.•Balance spatial topology, domain gaps, eases city class imbalance with Dice loss.
AbstractList	Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in the case studies across cities or regions, due to the lack of diverse RS information and cutting-edge solutions with high generalization ability. To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset), which consists of two cross-city scenes, i.e., Berlin-Augsburg (in Germany) and Beijing-Wuhan (in China). Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN for short, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion but also closing the gap derived from enormous differences of RS image representations between different cities by means of adversarial learning. In addition, the Dice loss is considered in HighDAN to alleviate the class imbalance issue caused by factors across cities. Extensive experiments conducted on the C2Seg dataset show the superiority of our HighDAN in terms of segmentation performance and generalization ability, compared to state-of-the-art competitors. The C2Seg dataset and the semantic segmentation toolbox (involving the proposed HighDAN) will be available publicly at https://github.com/danfenghong/RSE_Cross-city. •A new multimodal remote sensing benchmark for cross-city semantic segmentation.•Propose a high-resolution domain adaptation network for semantic segmentation.•Balance spatial topology, domain gaps, eases city class imbalance with Dice loss. Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in the case studies across cities or regions, due to the lack of diverse RS information and cutting-edge solutions with high generalization ability. To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset), which consists of two cross-city scenes, i.e., Berlin-Augsburg (in Germany) and Beijing-Wuhan (in China). Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN for short, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion but also closing the gap derived from enormous differences of RS image representations between different cities by means of adversarial learning. In addition, the Dice loss is considered in HighDAN to alleviate the class imbalance issue caused by factors across cities. Extensive experiments conducted on the C2Seg dataset show the superiority of our HighDAN in terms of segmentation performance and generalization ability, compared to state-of-the-art competitors. The C2Seg dataset and the semantic segmentation toolbox (involving the proposed HighDAN) will be available publicly at https://github.com/danfenghong/RSE_Cross-city.
ArticleNumber	113856
Author	Hong, Danfeng Li, Chenyu Zhang, Bing Li, Yuxuan Li, Hao Werner, Martin Yao, Jing Chanussot, Jocelyn Zipf, Alexander Zhu, Xiao Xiang
Author_xml	– sequence: 1 givenname: Danfeng surname: Hong fullname: Hong, Danfeng email: hongdf@aircas.ac.cn organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 2 givenname: Bing surname: Zhang fullname: Zhang, Bing email: zb@radi.ac.cn organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 3 givenname: Hao surname: Li fullname: Li, Hao email: hao_bgd.li@tum.de organization: Big Geospatial Data Management, Technical University of Munich, Munich 85521, Germany – sequence: 4 givenname: Yuxuan surname: Li fullname: Li, Yuxuan email: liyuxuan231@mails.ucas.ac.cn organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 5 givenname: Jing surname: Yao fullname: Yao, Jing email: yaojing@aircas.ac.cn organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 6 givenname: Chenyu surname: Li fullname: Li, Chenyu email: lichenyu@seu.edu.cn organization: School of Mathematics, Southeast University, Nanjing 210096, China – sequence: 7 givenname: Martin surname: Werner fullname: Werner, Martin email: martin.werner@tum.de organization: Big Geospatial Data Management, Technical University of Munich, Munich 85521, Germany – sequence: 8 givenname: Jocelyn surname: Chanussot fullname: Chanussot, Jocelyn email: jocelyn.chanussot@grenoble-inp.fr organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, Grenoble 38000, France – sequence: 9 givenname: Alexander surname: Zipf fullname: Zipf, Alexander email: zipf@uni-heidelberg.de organization: Institute of Geography, Heidelberg University, Heidelberg 69120, Germany – sequence: 10 givenname: Xiao Xiang surname: Zhu fullname: Zhu, Xiao Xiang email: xiaoxiang.zhu@tum.de organization: Data Science in Earth Observation, Technical University of Munich, Munich 80333, Germany
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Keywords	Deep learning High-resolution network Segmentation Multimodal benchmark datasets Domain adaptation Dice loss Land cover Remote sensing Cross-city
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Snippet	Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with...
SourceID	proquest crossref elsevier
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StartPage	113856
SubjectTerms	artificial intelligence China class Cross-city data collection Deep learning Dice loss domain Domain adaptation environment Germany High-resolution network Land cover Multimodal benchmark datasets Remote sensing Segmentation topology
Title	Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
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