{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:14:44Z","timestamp":1774120484695,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":77,"publisher":"ACM","funder":[{"name":"the Key Research and Development Program of Hainan Province","award":["No. ZDYF2023GXJS163"],"award-info":[{"award-number":["No. ZDYF2023GXJS163"]}]},{"name":"the Natural Science Foundation of Hainan University","award":["No. XJ2400009401"],"award-info":[{"award-number":["No. XJ2400009401"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.1145\/3746027.3755870","type":"proceedings-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T07:38:54Z","timestamp":1761377934000},"page":"2313-2322","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-view Graph Clustering with Dual Structure Awareness for Remote Sensing Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8642-8582","authenticated-orcid":false,"given":"Xin","family":"Peng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hainan University, Haikou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5914-3505","authenticated-orcid":false,"given":"Bowen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hainan University, Haikou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7201-9208","authenticated-orcid":false,"given":"Renxiang","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1353-2968","authenticated-orcid":false,"given":"Wenxuan","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hainan University, Haikou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"FCM: The fuzzy c-means clustering algorithm. Computers & geosciences","author":"Bezdek James C","year":"1984","unstructured":"James C Bezdek, Robert Ehrlich, and William Full. 1984. FCM: The fuzzy c-means clustering algorithm. Computers & geosciences, Vol. 10, 2-3 (1984), 191-203."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.07.003"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3018135"},{"key":"e_1_3_2_1_4_1","volume-title":"Learning unified anchor graph for joint clustering of hyperspectral and LiDAR data","author":"Cai Yaoming","year":"2024","unstructured":"Yaoming Cai, Zijia Zhang, Xiaobo Liu, Yao Ding, Fei Li, and Jinhua Tan. 2024. Learning unified anchor graph for joint clustering of hyperspectral and LiDAR data. IEEE Transactions on Neural Networks and Learning Systems (2024)."},{"key":"e_1_3_2_1_5_1","volume-title":"Advances in hyperspectral image classification: Earth monitoring with statistical learning methods","author":"Camps-Valls Gustavo","year":"2013","unstructured":"Gustavo Camps-Valls, Devis Tuia, Lorenzo Bruzzone, and Jon Atli Benediktsson. 2013. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, Vol. 31, 1 (2013), 45-54."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01536"},{"key":"e_1_3_2_1_7_1","first-page":"1","article-title":"Tensorial multiview subspace clustering for polarimetric hyperspectral images","volume":"60","author":"Chen Zhengyi","year":"2022","unstructured":"Zhengyi Chen, Chunmin Zhang, Tingkui Mu, and Yifan He. 2022. Tensorial multiview subspace clustering for polarimetric hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, Vol. 60 (2022), 1-13.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/int.22804"},{"key":"e_1_3_2_1_9_1","volume-title":"Multi-view graph convolutional network with spectral component decompose for remote sensing images classification","author":"Cheng Xijie","year":"2022","unstructured":"Xijie Cheng, Xiaohui He, Mengjia Qiao, Panle Li, Peng Chang, Tianhao Zhang, Xiaoyu Guo, Jinyong Wang, Zhihui Tian, and Guangsheng Zhou. 2022a. Multi-view graph convolutional network with spectral component decompose for remote sensing images classification. IEEE Transactions on Circuits and Systems for Video Technology (2022)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs9030243"},{"key":"e_1_3_2_1_11_1","volume-title":"Adaptive Homophily Clustering: A Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image. arXiv preprint arXiv:2501.01595","author":"Ding Yao","year":"2025","unstructured":"Yao Ding, Weijie Kang, Aitao Yang, Zhili Zhang, Junyang Zhao, Jie Feng, Danfeng Hong, and Qinhe Zheng. 2025. Adaptive Homophily Clustering: A Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image. arXiv preprint arXiv:2501.01595 (2025)."},{"key":"e_1_3_2_1_12_1","first-page":"22667","article-title":"SLAPS: Self-supervision improves structure learning for graph neural networks","volume":"34","author":"Fatemi Bahare","year":"2021","unstructured":"Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi. 2021. SLAPS: Self-supervision improves structure learning for graph neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021), 22667-22681.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_13_1","volume-title":"Subspace-contrastive multi-view clustering. ACM Transactions on Knowledge Discovery from Data","author":"Fu Lele","year":"2024","unstructured":"Lele Fu, Sheng Huang, Lei Zhang, Jinghua Yang, Zibin Zheng, Chuanfu Zhang, and Chuan Chen. 2024. Subspace-contrastive multi-view clustering. ACM Transactions on Knowledge Discovery from Data, Vol. 18, 9 (2024), 1-35."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2449668"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14133216"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10447080"},{"key":"e_1_3_2_1_17_1","volume-title":"Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks","author":"Guan Renxiang","year":"2024","unstructured":"Renxiang Guan, Zihao Li, Wenxuan Tu, Jun Wang, Yue Liu, Xianju Li, Chang Tang, and Ruyi Feng. 2024b. Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks. IEEE Transactions on Geoscience and Remote Sensing (2024)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3580139"},{"key":"e_1_3_2_1_19_1","volume-title":"An effective global structure-aware feature aggregation network for multi-modal medical clustering. Expert Systems with Applications","author":"Guan Renxiang","year":"2025","unstructured":"Renxiang Guan, Hao Quan, Deliang Li, and Dayu Hu. 2025b. An effective global structure-aware feature aggregation network for multi-modal medical clustering. Expert Systems with Applications (2025), 126835."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3464648"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i16.33861"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.05.011"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae483"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5194\/essd-15-113-2023"},{"key":"e_1_3_2_1_26_1","first-page":"1","article-title":"Graph convolutional sparse subspace coclustering with nonnegative orthogonal factorization for large hyperspectral images","volume":"60","author":"Huang Nan","year":"2021","unstructured":"Nan Huang, Liang Xiao, Jianjun Liu, and Jocelyn Chanussot. 2021. Graph convolutional sparse subspace coclustering with nonnegative orthogonal factorization for large hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, Vol. 60 (2021), 1-16.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25960"},{"key":"e_1_3_2_1_28_1","volume-title":"An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges. Information fusion","author":"Imani Maryam","year":"2020","unstructured":"Maryam Imani and Hassan Ghassemian. 2020. An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges. Information fusion, Vol. 59 (2020), 59-83."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28710"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.102926"},{"key":"e_1_3_2_1_32_1","volume-title":"Superpixel Prior Cluster-level Contrastive Clustering Network for Large-scale Urban Hyperspectral Images and Vehicle Detection","author":"Li Tiancong","year":"2024","unstructured":"Tiancong Li, Yaoming Cai, Yongshan Zhang, Zhihua Cai, Guozhu Jiang, and Xiaobo Liu. 2024a. Superpixel Prior Cluster-level Contrastive Clustering Network for Large-scale Urban Hyperspectral Images and Vehicle Detection. IEEE Transactions on Vehicular Technology (2024)."},{"key":"e_1_3_2_1_33_1","first-page":"30306","article-title":"Gslb: The graph structure learning benchmark","volume":"36","author":"Li Zhixun","year":"2023","unstructured":"Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, et al., 2023. Gslb: The graph structure learning benchmark. Advances in Neural Information Processing Systems, Vol. 36 (2023), 30306-30318.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3238416"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i18.34077"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512206"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2025.3533301"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512186"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3374597"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2021.3075491"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2024.3377280"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3244397"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3132856"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_19"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20335"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3073269","article-title":"A relation-augmented embedded graph attention network for remote sensing object detection","volume":"60","author":"Tian Shu","year":"2021","unstructured":"Shu Tian, Lihong Kang, Xiangwei Xing, Jing Tian, Chunzhuo Fan, and Ye Zhang. 2021. A relation-augmented embedded graph attention network for remote sensing object detection. IEEE Transactions on Geoscience and Remote Sensing, Vol. 60 (2021), 1-18.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29464"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3335222"},{"key":"e_1_3_2_1_49_1","volume-title":"Revisiting initializing then refining: An incomplete and missing graph imputation network","author":"Tu Wenxuan","year":"2024","unstructured":"Wenxuan Tu, Bin Xiao, Xinwang Liu, Sihang Zhou, Zhiping Cai, and Jieren Cheng. 2024b. Revisiting initializing then refining: An incomplete and missing graph imputation network. IEEE Transactions on Neural Networks and Learning Systems (2024)."},{"key":"e_1_3_2_1_50_1","volume-title":"Wage: Weight-sharing attribute-missing graph autoencoder","author":"Tu Wenxuan","year":"2025","unstructured":"Wenxuan Tu, Sihang Zhou, Xinwang Liu, Zhiping Cai, Yawei Zhao, Yue Liu, and Kunlun He. 2025. Wage: Weight-sharing attribute-missing graph autoencoder. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17198"},{"key":"e_1_3_2_1_52_1","first-page":"3494","article-title":"Initializing Then Refining: A Simple Graph Attribute Imputation Network","author":"Tu Wenxuan","year":"2022","unstructured":"Wenxuan Tu, Sihang Zhou, Xinwang Liu, Yue Liu, Zhiping Cai, En Zhu, Changwang Zhang, and Jieren Cheng. 2022. Initializing Then Refining: A Simple Graph Attribute Imputation Network.. In IJCAI. 3494-3500.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_53_1","volume-title":"A tutorial on spectral clustering. Statistics and computing","author":"Luxburg Ulrike Von","year":"2007","unstructured":"Ulrike Von Luxburg. 2007. A tutorial on spectral clustering. Statistics and computing, Vol. 17 (2007), 395-416."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3378194"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26201"},{"key":"e_1_3_2_1_56_1","volume-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 5055-5063","author":"Wang Jing","year":"2024","unstructured":"Jing Wang and Songhe Feng. 2024. Contrastive and view-interaction structure learning for multi-view clustering. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 5055-5063."},{"key":"e_1_3_2_1_57_1","volume-title":"Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering. In 2024 International Joint Conference on Neural Networks (IJCNN). 1-8.","author":"Wang Jingxin","year":"2024","unstructured":"Jingxin Wang, Renxiang Guan, Kainan Gao, Zihao Li, Hao Li, Xianju Li, and Chang Tang. 2024. Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering. In 2024 International Joint Conference on Neural Networks (IJCNN). 1-8."},{"key":"e_1_3_2_1_58_1","first-page":"5882","article-title":"Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences","volume":"35","author":"Wang Siwei","year":"2022","unstructured":"Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, and En Zhu. 2022. Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences. Advances in Neural Information Processing Systems, Vol. 35 (2022), 5882-5895.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_59_1","volume-title":"Nodeformer: A scalable graph structure learning transformer for node classification. In Advances in Neural Information Processing Systems.","author":"Wu Qitian","year":"2022","unstructured":"Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, and Junchi Yan. 2022. Nodeformer: A scalable graph structure learning transformer for node classification. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121186"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3094296"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3193569"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01558"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01902"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2637824"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.5555\/3692070.3694438"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3332335"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29594"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3312979"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2020.3032575"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2877335"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2022.3145854"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102400"},{"key":"e_1_3_2_1_74_1","volume-title":"MMAGL: Multi-objective Multi-view Attributed Graph Learning for Joint Clustering of Hyperspectral and LiDAR Data","author":"Zhang Zijia","year":"2025","unstructured":"Zijia Zhang, Yaoming Cai, Wenyin Gong, Xiaobo Liu, Cheng Zeng, and Gan Yu. 2025. MMAGL: Multi-objective Multi-view Attributed Graph Learning for Joint Clustering of Hyperspectral and LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing (2025)."},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3042202"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/JMASS.2023.3244848"},{"key":"e_1_3_2_1_77_1","volume-title":"A survey on graph structure learning: Progress and opportunities. arXiv preprint arXiv:2103.03036","author":"Zhu Yanqiao","year":"2021","unstructured":"Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, and Shu Wu. 2021. A survey on graph structure learning: Progress and opportunities. arXiv preprint arXiv:2103.03036 (2021)."}],"event":{"name":"MM '25: The 33rd ACM International Conference on Multimedia","location":"Dublin Ireland","acronym":"MM '25","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 33rd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3755870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:12:27Z","timestamp":1765339947000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746027.3755870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":77,"alternative-id":["10.1145\/3746027.3755870","10.1145\/3746027"],"URL":"https:\/\/doi.org\/10.1145\/3746027.3755870","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}