{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:25:20Z","timestamp":1776101120467,"version":"3.50.1"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371338"],"award-info":[{"award-number":["42371338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013099","name":"Scientific Research Fund of Liaoning Provincial Education Department","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013099","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","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":[[2026,6]]},"DOI":"10.1016\/j.engappai.2026.114488","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T09:54:35Z","timestamp":1773395675000},"page":"114488","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Perceptive scale and selective attention few-shot learning network for hyperspectral and light detection and ranging fusion classification"],"prefix":"10.1016","volume":"173","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7600-9939","authenticated-orcid":false,"given":"Xianghai","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyue","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.engappai.2026.114488_b1","doi-asserted-by":"crossref","first-page":"3696","DOI":"10.3390\/s25123696","article-title":"Spaceborne LiDAR systems: Evolution, capabilities, and challenges","volume":"25","author":"Bolcek","year":"2025","journal-title":"Sensors"},{"issue":"6","key":"10.1016\/j.engappai.2026.114488_b2","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/JSTARS.2014.2305441","article-title":"Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest","volume":"7","author":"Debes","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.114488_b3","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.engappai.2026.114488_b4","series-title":"Technical report: Scene label ground truth map for MUUFL Gulfport data set","author":"Du","year":"2017"},{"issue":"24","key":"10.1016\/j.engappai.2026.114488_b5","doi-asserted-by":"crossref","first-page":"4034","DOI":"10.3390\/rs12244034","article-title":"Multilevel structure extraction-based multi-sensor data fusion","volume":"12","author":"Duan","year":"2020","journal-title":"Remote. Sens."},{"key":"10.1016\/j.engappai.2026.114488_b6","first-page":"1","article-title":"Cross-domain few-shot learning based on feature disentanglement for hyperspectral image classification","volume":"62","author":"Feng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114488_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111190","article-title":"S2EFT: Spectral-spatial-elevation fusion transformer for hyperspectral image and LiDAR classification","volume":"283","author":"Feng","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114488_b8","series-title":"MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set","author":"Gader","year":"2013"},{"issue":"6","key":"10.1016\/j.engappai.2026.114488_b9","doi-asserted-by":"crossref","first-page":"923","DOI":"10.3390\/rs12060923","article-title":"Deep relation network for hyperspectral image few-shot classification","volume":"12","author":"Gao","year":"2020","journal-title":"Remote. Sens."},{"key":"10.1016\/j.engappai.2026.114488_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108970","article-title":"Cross-domain few-shot fault diagnosis based on meta-learning and domain adversarial graph convolutional network","volume":"136","author":"Hu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.engappai.2026.114488_b11","doi-asserted-by":"crossref","first-page":"94","DOI":"10.3390\/rs16010094","article-title":"Attention-guided fusion and classification for hyperspectral and LiDAR data","volume":"16","author":"Huang","year":"2024","journal-title":"Remote. Sens."},{"key":"10.1016\/j.engappai.2026.114488_b12","first-page":"1","article-title":"Deep cross-domain few-shot learning for hyperspectral image classification","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"9","key":"10.1016\/j.engappai.2026.114488_b13","doi-asserted-by":"crossref","first-page":"2246","DOI":"10.3390\/rs14092246","article-title":"Graph-based deep multitask few-shot learning for hyperspectral image classification","volume":"14","author":"Li","year":"2022","journal-title":"Remote. Sens."},{"key":"10.1016\/j.engappai.2026.114488_b14","series-title":"2021 IEEE\/CVF International Conference on Computer Vision","first-page":"9992","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"issue":"4","key":"10.1016\/j.engappai.2026.114488_b15","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TGRS.2018.2872830","article-title":"Deep few-shot learning for hyperspectral image classification","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114488_b16","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.inffus.2022.12.020","article-title":"Coupled adversarial learning for fusion classification of hyperspectral and LiDAR data","volume":"93","author":"Lu","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.114488_b17","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2023.03.025","article-title":"Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions","volume":"536","author":"Moharram","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.114488_b18","series-title":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","first-page":"1","article-title":"Classification of pixel-level fused hyperspectral and lidar data using deep convolutional neural networks","author":"Morchhale","year":"2016"},{"issue":"3","key":"10.1016\/j.engappai.2026.114488_b19","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1080\/01431161.2024.2429784","article-title":"A critical review on multi-sensor and multi-platform remote sensing data fusion approaches: current status and prospects","volume":"46","author":"Samadzadegan","year":"2025","journal-title":"Int. J. Remote. Sensing"},{"key":"10.1016\/j.engappai.2026.114488_b20","series-title":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA","first-page":"5978","article-title":"A new multi-level attention feature fusion method for hyperspectral and lidar data joint classification","author":"Song","year":"2023"},{"key":"10.1016\/j.engappai.2026.114488_b21","first-page":"1","article-title":"MCFNet: Multiscale cross-domain fusion network for HSI and LiDAR data joint classification","volume":"63","author":"Song","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114488_b22","series-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","first-page":"3929","article-title":"MEDA: Meta-learning with data augmentation for few-shot text classification","author":"Sun","year":"2021"},{"key":"10.1016\/j.engappai.2026.114488_b23","series-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.engappai.2026.114488_b24","first-page":"1","article-title":"BiG-FSLF: A cross heterogeneous domain few-shot learning framework based on bidirectional generation for hyperspectral image change detection","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"10.1016\/j.engappai.2026.114488_b25","doi-asserted-by":"crossref","first-page":"4801","DOI":"10.1109\/TCSVT.2025.3525734","article-title":"S3F2Net: Spatial-spectral-structural feature fusion network for hyperspectral image and LiDAR data classification","volume":"35","author":"Wang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"10.1016\/j.engappai.2026.114488_b26","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","article-title":"Multisource remote sensing data classification based on convolutional neural network","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114488_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107125","article-title":"Adaptive federated few-shot feature learning with prototype rectification","volume":"126","author":"Yang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114488_b28","first-page":"1","article-title":"Single-stream CNN with learnable architecture for multisource remote sensing data","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114488_b29","series-title":"2021 IEEE 18th International Symposium on Biomedical Imaging","first-page":"262","article-title":"A location-sensitive local prototype network for few-shot medical image segmentation","author":"Yu","year":"2021"},{"key":"10.1016\/j.engappai.2026.114488_b30","first-page":"1","article-title":"Multistage network with dual-metric for few-shot hyperspectral image classification","volume":"61","author":"Zeng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114488_b31","first-page":"1","article-title":"Information fusion for classification of hyperspectral and LiDAR data using IP-CNN","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"10.1016\/j.engappai.2026.114488_b32","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1109\/TNNLS.2022.3185795","article-title":"Graph information aggregation cross-domain few-shot learning for hyperspectral image classification","volume":"35","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"10.1016\/j.engappai.2026.114488_b33","article-title":"Artificial intelligence for geoscience: Progress, challenges, and perspectives","volume":"5","author":"Zhao","year":"2024","journal-title":"Innov."},{"key":"10.1016\/j.engappai.2026.114488_b34","first-page":"1","article-title":"Joint classification of hyperspectral and LiDAR data using a hierarchical CNN and transformer","volume":"61","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007694?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007694?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T16:31:46Z","timestamp":1776097906000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626007694"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":34,"alternative-id":["S0952197626007694"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114488","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Perceptive scale and selective attention few-shot learning network for hyperspectral and light detection and ranging fusion 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.2026.114488","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":"114488"}}