{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:38:54Z","timestamp":1743100734297,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031783531"},{"type":"electronic","value":"9783031783548"}],"license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78354-8_29","type":"book-chapter","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T10:28:31Z","timestamp":1733221711000},"page":"458-474","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spectral Aggregation Cross-Square Transformer for Hyperspectral Image Denoising"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6607-861X","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4146-2661","authenticated-orcid":false,"given":"Yantao","family":"Ji","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0469-528X","authenticated-orcid":false,"given":"Jiahua","family":"Xiao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5489-8288","authenticated-orcid":false,"given":"Yu","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0316-6631","authenticated-orcid":false,"given":"Peilin","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2527-8335","authenticated-orcid":false,"given":"Haiwei","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3462-8472","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Jong-Il Park, Moon-Hyun Lee, Michael\u00a0D Grossberg, and Shree\u00a0K Nayar. Multispectral imaging using multiplexed illumination. In 2007 IEEE 11th International Conference on Computer Vision, pages 1\u20138. IEEE, 2007","DOI":"10.1109\/ICCV.2007.4409090"},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"3539","DOI":"10.1109\/JSTARS.2022.3170057","volume":"15","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Li, D., Hanjie, W., Li, X., Kong, F., Wang, Q.: Multiple spectral-spatial representation based on tensor decomposition for hsi anomaly detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15, 3539\u20133551 (2022)","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Priyanka Sahu, Amit\u00a0Prakash Singh, Anuradha Chug, and Dinesh Singh. A systematic literature review of machine learning techniques deployed in agriculture: A case study of banana crop. IEEE Access, 10:87333\u201387360, 2022","DOI":"10.1109\/ACCESS.2022.3199926"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Juntao Guan, Rui Lai, Huanan Li, Yintang Yang, and Lin Gu. Dnrcnn: Deep recurrent convolutional neural network for hsi destriping. IEEE Transactions on Neural Networks and Learning Systems, 2022","DOI":"10.1109\/TNNLS.2022.3142425"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Salman Khan, Muzammal Naseer, Munawar Hayat, Syed\u00a0Waqas Zamir, Fahad\u00a0Shahbaz Khan, and Mubarak Shah. Transformers in vision: A survey. ACM computing surveys (CSUR), 54(10s):1\u201341, 2022","DOI":"10.1145\/3505244"},{"key":"29_CR6","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.isatra.2019.02.018","volume":"92","author":"X Ping","year":"2019","unstructured":"Ping, X., Chen, B., Xue, L., Zhang, J., Zhu, L., Duan, H.: A new mnf-bm4d denoising algorithm based on guided filtering for hyperspectral images. ISA Trans. 92, 315\u2013324 (2019)","journal-title":"ISA Trans."},{"issue":"3","key":"29_CR7","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1109\/JSTARS.2018.2800701","volume":"11","author":"W He","year":"2018","unstructured":"He, W., Zhang, H., Shen, H., Zhang, L.: Hyperspectral image denoising using local low-rank matrix recovery and global spatial-spectral total variation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(3), 713\u2013729 (2018)","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"29_CR8","doi-asserted-by":"publisher","first-page":"6099","DOI":"10.1109\/JSTARS.2022.3192484","volume":"15","author":"Y Sun","year":"2022","unstructured":"Sun, Y., Huang, J., Zhao, L., Kai, H.: Hyperspectral snapshot compressive imaging with dense back-projection joint attention network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15, 6099\u20136109 (2022)","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"2","key":"29_CR9","doi-asserted-by":"publisher","first-page":"490","DOI":"10.3390\/rs15020490","volume":"15","author":"X Wei","year":"2023","unstructured":"Wei, X., Xiao, J., Gong, Y.: Blind hyperspectral image denoising with degradation information learning. Remote Sensing 15(2), 490 (2023)","journal-title":"Remote Sensing"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Jiahua Xiao, Yantao Ji, and Xing Wei. Hyperspectral image denoising with spectrum alignment. In Proceedings of the 31st ACM International Conference on Multimedia, pages 5495\u20135503, 2023","DOI":"10.1145\/3581783.3612016"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Jiahua Xiao and Xing Wei. Hyperspectral image denoising using uncertainty-aware adjustor. In IJCAI, pages 1560\u20131568, 2023","DOI":"10.24963\/ijcai.2023\/173"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Jiahua Xiao, Yang Liu, Shizhou Zhang, and Xing Wei. Bridging fourier and spatial-spectral domains for hyperspectral image denoising. In ACM Multimedia, 2024","DOI":"10.1145\/3664647.3681461"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Qiang Zhang, Yaming Zheng, Qiangqiang Yuan, Meiping Song, Haoyang Yu, and Yi\u00a0Xiao. Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven. IEEE Transactions on Neural Networks and Learning Systems, 2023","DOI":"10.1109\/TNNLS.2023.3278866"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Swalpa\u00a0Kumar Roy, Ankur Deria, Chiranjibi Shah, Juan\u00a0M Haut, Qian Du, and Antonio Plaza. Spectral\u2013spatial morphological attention transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 61:1\u201315, 2023","DOI":"10.1109\/TGRS.2023.3242346"},{"key":"29_CR15","doi-asserted-by":"publisher","first-page":"9266","DOI":"10.1109\/JSTARS.2022.3216335","volume":"15","author":"W He","year":"2022","unstructured":"He, W., Huang, W., Liao, S., Zhen, X., Yan, J.: Csit: A multiscale vision transformer for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15, 9266\u20139277 (2022)","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Mathias Gehrig and Davide Scaramuzza. Recurrent vision transformers for object detection with event cameras. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 13884\u201313893, 2023","DOI":"10.1109\/CVPR52729.2023.01334"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Dahun Kim, Anelia Angelova, and Weicheng Kuo. Region-aware pretraining for open-vocabulary object detection with vision transformers. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 11144\u201311154, 2023","DOI":"10.1109\/CVPR52729.2023.01072"},{"key":"29_CR18","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.1609\/aaai.v37i1.25221","volume":"37","author":"M Li","year":"2023","unstructured":"Li, M., Ying, F., Zhang, Y.: Spatial-spectral transformer for hyperspectral image denoising. In Proceedings of the AAAI Conference on Artificial Intelligence 37, 1368\u20131376 (2023)","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Miaoyu Li, Ji\u00a0Liu, Ying Fu, Yulun Zhang, and Dejing Dou. Spectral enhanced rectangle transformer for hyperspectral image denoising. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 5805\u20135814, 2023","DOI":"10.1109\/CVPR52729.2023.00562"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Yuanhao Cai, Jing Lin, Xiaowan Hu, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, and Luc Van\u00a0Gool. Coarse-to-fine sparse transformer for hyperspectral image reconstruction. In European Conference on Computer Vision, pages 686\u2013704. Springer, 2022","DOI":"10.1007\/978-3-031-19790-1_41"},{"key":"29_CR21","unstructured":"Huaibo Huang, Xiaoqiang Zhou, Jie Cao, Ran He, and Tieniu Tan. Vision transformer with super token sampling. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 22690\u201322699, 2023"},{"issue":"1","key":"29_CR22","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1109\/TNNLS.2020.2978756","volume":"32","author":"K Wei","year":"2020","unstructured":"Wei, K., Ying, F., Huang, H.: 3-d quasi-recurrent neural network for hyperspectral image denoising. IEEE transactions on neural networks and learning systems 32(1), 363\u2013375 (2020)","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"29_CR23","doi-asserted-by":"publisher","unstructured":"Arad, B., Ben-Shahar, O.: Sparse Recovery of Hyperspectral Signal from Natural RGB Images. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 19\u201334. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_2","DOI":"10.1007\/978-3-319-46478-7_2"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Choi, I.: MH Kim, D Gutierrez, DS Jeon, and G Nam. High-quality hyperspectral reconstruction using a spectral prior, Technical report (2017)","DOI":"10.1145\/3130800.3130810"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Paolo Gamba. A collection of data for urban area characterization. In IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, volume\u00a01. IEEE, 2004","DOI":"10.1109\/IGARSS.2004.1368947"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Volodymyr Mnih and Geoffrey\u00a0E Hinton. Learning to detect roads in high-resolution aerial images. In European conference on computer vision, pages 210\u2013223. Springer, 2010","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Tao Zhang, Ying Fu, and Cheng Li. Hyperspectral image denoising with realistic data. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pages 2248\u20132257, October 2021","DOI":"10.1109\/ICCV48922.2021.00225"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Zhou Wang, Alan\u00a0C Bovik, Hamid\u00a0R Sheikh, and Eero\u00a0P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600\u2013612, 2004","DOI":"10.1109\/TIP.2003.819861"},{"key":"29_CR29","unstructured":"Roberta\u00a0H Yuhas, Joseph\u00a0W Boardman, and Alexander\u00a0FH Goetz. Determination of semi-arid landscape endmembers and seasonal trends using convex geometry spectral unmixing techniques. In JPL, Summaries of the 4th Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop, 1993"},{"issue":"4","key":"29_CR30","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/JSTARS.2017.2779539","volume":"11","author":"Y Wang","year":"2017","unstructured":"Wang, Y., Peng, J., Zhao, Q., Leung, Y., Zhao, X.-L., Meng, D.: Hyperspectral image restoration via total variation regularized low-rank tensor decomposition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(4), 1227\u20131243 (2017)","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"3","key":"29_CR31","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1109\/JSTARS.2018.2800701","volume":"11","author":"W He","year":"2018","unstructured":"He, W., Zhang, H., Shen, H., Zhang, L.: Hyperspectral image denoising using local low-rank matrix recovery and global spatial-spectral total variation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(3), 713\u2013729 (2018)","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"29_CR32","first-page":"1","volume":"60","author":"X Cao","year":"2021","unstructured":"Cao, X., Xueyang, F., Chen, X., Meng, D.: Deep spatial-spectral global reasoning network for hyperspectral image denoising. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"29_CR33","first-page":"1","volume":"60","author":"F Xiong","year":"2021","unstructured":"Xiong, F., Zhou, J., Zhao, Q., Jianfeng, L., Qian, Y.: Mac-net: Model-aided nonlocal neural network for hyperspectral image denoising. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"29_CR34","first-page":"5430","volume":"34","author":"T Bodrito","year":"2021","unstructured":"Bodrito, T., Zouaoui, A., Chanussot, J., Mairal, J.: A trainable spectral-spatial sparse coding model for hyperspectral image restoration. Adv. Neural. Inf. Process. Syst. 34, 5430\u20135442 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78354-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T11:32:37Z","timestamp":1733225557000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78354-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"ISBN":["9783031783531","9783031783548"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78354-8_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"4 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}