{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T21:59:01Z","timestamp":1782856741101,"version":"3.54.5"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2025A1515011365"],"award-info":[{"award-number":["2025A1515011365"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010909","name":"Excellent Young Scientists Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010909","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62301444"],"award-info":[{"award-number":["62301444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115483","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T10:19:14Z","timestamp":1782469154000},"page":"115483","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P3","title":["Spectral consistency learning for cross-domain hyperspectral image classification"],"prefix":"10.1016","volume":"181","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6360-8262","authenticated-orcid":false,"given":"Zhiyu","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dandan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115483_b1","first-page":"1","article-title":"Spectral\u2013spatial adversarial multidomain synthesis network for cross-scene hyperspectral image classification","volume":"62","author":"Chen","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b2","first-page":"1","article-title":"Adaptive homophily clustering: Structure homophily graph learning with adaptive filter for hyperspectral image","volume":"63","author":"Ding","year":"2025","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b3","doi-asserted-by":"crossref","first-page":"8251","DOI":"10.1109\/TMM.2025.3604954","article-title":"SLCGC: A lightweight self-supervised low-pass contrastive graph clustering network for hyperspectral images","volume":"27","author":"Ding","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.engappai.2026.115483_b4","first-page":"1","article-title":"Self-supervised locality preserving low-pass graph convolutional embedding for large-scale hyperspectral image clustering","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b5","first-page":"1","article-title":"Spectral\u2013spatial enhancement and causal constraint for hyperspectral image cross-scene classification","volume":"62","author":"Dong","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b6","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: Proc. Int. Conf. Learn. Represent.."},{"issue":"5","key":"10.1016\/j.engappai.2026.115483_b7","doi-asserted-by":"crossref","first-page":"3246","DOI":"10.1109\/TGRS.2019.2951445","article-title":"Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"10.1016\/j.engappai.2026.115483_b8","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.1109\/TPAMI.2024.3362475","article-title":"Spectralgpt: Spectral remote sensing foundation model","volume":"46","author":"Hong","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.115483_b9","first-page":"1","article-title":"Two-branch attention adversarial domain adaptation network for hyperspectral image classification","volume":"60","author":"Huang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b10","first-page":"1","article-title":"Positive-incentive noise","author":"Li","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.engappai.2026.115483_b11","doi-asserted-by":"crossref","unstructured":"Li, L., Gao, K., Cao, J., Huang, Z., Weng, Y., Mi, X., Yu, Z., Li, X., Xia, B., 2021. Progressive Domain Expansion Network for Single Domain Generalization. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit.. pp. 224\u2013233.","DOI":"10.1109\/CVPR46437.2021.00029"},{"issue":"4","key":"10.1016\/j.engappai.2026.115483_b12","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TCSVT.2022.3218284","article-title":"Exploring the relationship between center and neighborhoods: Central vector oriented self-similarity network for hyperspectral image classification","volume":"33","author":"Li","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.115483_b13","unstructured":"Li, S., You, C., Guruganesh, G., Ainslie, J., Ontanon, S., Zaheer, M., Sanghai, S., Yang, Y., Kumar, S., Bhojanapalli, S., 2024. Functional Interpolation for Relative Positions improves Long Context Transformers. In: Proc. Int. Conf. Learn. Represent.."},{"key":"10.1016\/j.engappai.2026.115483_b14","doi-asserted-by":"crossref","unstructured":"Li, Z., Yuan, Y., Ma, D., 2021. One-Stage Detector from Coarse to Fine for Rotating Object of Remote Sensing. In: Proc. IEEE Int. Geosci. Remote. Sens. Symp.. pp. 5307\u20135310.","DOI":"10.1109\/IGARSS47720.2021.9553926"},{"key":"10.1016\/j.engappai.2026.115483_b15","doi-asserted-by":"crossref","first-page":"6624","DOI":"10.1109\/JSTARS.2021.3091591","article-title":"Spectral shift mitigation for cross-scene hyperspectral imagery classification","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b16","series-title":"Learning to Encode Position for Transformer with Continuous Dynamical Model","author":"Liu","year":"2020"},{"key":"10.1016\/j.engappai.2026.115483_b17","doi-asserted-by":"crossref","unstructured":"Nam, H., Lee, H., Park, J., Yoon, W., Yoo, D., 2021. Reducing Domain Gap by Reducing Style Bias. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit.. pp. 8686\u20138695.","DOI":"10.1109\/CVPR46437.2021.00858"},{"key":"10.1016\/j.engappai.2026.115483_b18","first-page":"1","article-title":"Multisource domain generalization two-branch network for hyperspectral image cross-domain classification","volume":"21","author":"Qi","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.engappai.2026.115483_b19","series-title":"Self-Attention with Relative Position Representations","author":"Shaw","year":"2018"},{"key":"10.1016\/j.engappai.2026.115483_b20","first-page":"1","article-title":"Ensemble alignment subspace adaptation method for cross-scene classification","volume":"20","author":"Song","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.engappai.2026.115483_b21","first-page":"1","article-title":"Unsupervised joint adversarial domain adaptation for cross-scene hyperspectral image classification","volume":"60","author":"Tang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b22","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., 2017. Attention is all you need. In: Proc. Adv. Neural Inf. Process. Syst.. pp. 6000\u20136010."},{"key":"10.1016\/j.engappai.2026.115483_b23","doi-asserted-by":"crossref","first-page":"1802","DOI":"10.1109\/TIP.2025.3599929","article-title":"GIDDM: Generating labels with diffusion model to promote cross-domain open-set image recognition","volume":"35","author":"Wang","year":"2026","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.115483_b24","series-title":"HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model","author":"Wang","year":"2024"},{"issue":"8","key":"10.1016\/j.engappai.2026.115483_b25","first-page":"8052","article-title":"Generalizing to unseen domains: A survey on domain generalization","volume":"35","author":"Wang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.engappai.2026.115483_b26","first-page":"1","article-title":"Inducing causal meta-knowledge from virtual domain: Causal meta-generalization for hyperspectral domain generalization","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b27","doi-asserted-by":"crossref","unstructured":"Wang, Z., Luo, Y., Qiu, R., Huang, Z., Baktash, M., 2021. Learning to Diversify for Single Domain Generalization. In: Proc. IEEE Int. Conf. Comput. Vis.. pp. 814\u2013823.","DOI":"10.1109\/ICCV48922.2021.00087"},{"key":"10.1016\/j.engappai.2026.115483_b28","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1109\/TIP.2019.2948480","article-title":"Class-specific reconstruction transfer learning for visual recognition across domains","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.115483_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113972","article-title":"Domain adaptation network based on multi-level feature alignment constraints for cross scene hyperspectral image classification","volume":"325","author":"Wu","year":"2025","journal-title":"Know.-Based Syst."},{"key":"10.1016\/j.engappai.2026.115483_b30","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.dt.2022.02.007","article-title":"Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification","volume":"23","author":"Yao","year":"2023","journal-title":"Def. Technol."},{"key":"10.1016\/j.engappai.2026.115483_b31","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Chen, Y., Huang, M., 2019. Transfer Learning with Dynamic Adversarial Adaptation Network. In: Proc. IEEE Int. Conf. Data Min.. pp. 778\u2013786.","DOI":"10.1109\/ICDM.2019.00088"},{"key":"10.1016\/j.engappai.2026.115483_b32","first-page":"1","article-title":"Proxy-based deep learning framework for spectral-spatial hyperspectral image classification: Efficient and robust","volume":"60","author":"Yuan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b33","first-page":"1","article-title":"NACAD: A noise-adaptive context-aware detector for remote sensing small objects","volume":"61","author":"Yuan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.115483_b34","first-page":"1","article-title":"RSVG: Exploring data and models for visual grounding on remote sensing data","volume":"61","author":"Zhan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"issue":"12","key":"10.1016\/j.engappai.2026.115483_b35","doi-asserted-by":"crossref","first-page":"12038","DOI":"10.1109\/TCSVT.2025.3586282","article-title":"Cross-domain hyperspectral image classification based on bi-directional domain adaptation","volume":"35","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.115483_b36","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TIP.2023.3243853","article-title":"Single-source domain expansion network for cross-scene hyperspectral image classification","volume":"32","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.115483_b37","article-title":"Locality robust domain adaptation for cross-scene hyperspectral image classification","volume":"238","author":"Zhang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.115483_b38","series-title":"Spectralx: Parameter-efficient domain generalization for spectral remote sensing foundation models","author":"Zhang","year":"2025"},{"issue":"6","key":"10.1016\/j.engappai.2026.115483_b39","doi-asserted-by":"crossref","first-page":"2817","DOI":"10.1109\/TNNLS.2021.3109872","article-title":"Topological structure and semantic information transfer network for cross-scene hyperspectral image classification","volume":"34","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.engappai.2026.115483_b40","series-title":"Units: Unified spatio-temporal generative model for remote sensing","author":"Zhang","year":"2026"},{"key":"10.1016\/j.engappai.2026.115483_b41","first-page":"1","article-title":"Language-aware domain generalization network for cross-scene hyperspectral image classification","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.115483_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129690","article-title":"Unbiased representation learning via feature decoupling network for cross-scene hyperspectral image classification","volume":"298","author":"Zhao","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.115483_b43","first-page":"1","article-title":"Locally linear unbiased randomization network for cross-scene hyperspectral image classification","volume":"61","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.115483_b44","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neunet.2019.07.010","article-title":"Multi-representation adaptation network for cross-domain image classification","volume":"119","author":"Zhu","year":"2019","journal-title":"Neural Netw.","ISSN":"https:\/\/id.crossref.org\/issn\/0893-6080","issn-type":"print"},{"issue":"4","key":"10.1016\/j.engappai.2026.115483_b45","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TNNLS.2020.2988928","article-title":"Deep subdomain adaptation network for image classification","volume":"32","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626017677?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626017677?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:52:08Z","timestamp":1782852728000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626017677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":45,"alternative-id":["S0952197626017677"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115483","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Spectral consistency learning for cross-domain hyperspectral image classification","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115483","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115483"}}