{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:16:12Z","timestamp":1777126572724,"version":"3.51.4"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"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","award":["JYTZD2023101"],"award-info":[{"award-number":["JYTZD2023101"]}],"id":[{"id":"10.13039\/501100013099","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Dalian University","award":["Discipline Notice of Dalian University 2025[006]"],"award-info":[{"award-number":["Discipline Notice of Dalian University 2025[006]"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s10489-026-07126-z","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T03:18:21Z","timestamp":1771643901000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FCFCNN: frequency coupled fusion convolutional neural network for hyperspectral and LiDAR data classification"],"prefix":"10.1007","volume":"56","author":[{"given":"Yin","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yining","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanming","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianghai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"7126_CR1","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.isprsjprs.2021.05.011","volume":"178","author":"D Hong","year":"2021","unstructured":"Hong D, Hu J, Yao J, Chanussot J, Zhu XX (2021) Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS J Photogrammetry Remote Sens 178:68\u201380","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"issue":"14","key":"7126_CR2","doi-asserted-by":"publisher","first-page":"3492","DOI":"10.3390\/rs14143492","volume":"14","author":"Y Yuan","year":"2022","unstructured":"Yuan Y, Meng X, Sun W, Yang G, Wang L, Peng J, Wang Y (2022) Multi-resolution collaborative fusion of sar, multispectral and hyperspectral images for coastal wetlands mapping. Remote Sens 14(14):3492","journal-title":"Remote Sens"},{"key":"7126_CR3","doi-asserted-by":"publisher","first-page":"5721","DOI":"10.1109\/JSTARS.2022.3190316","volume":"15","author":"X Zhao","year":"2022","unstructured":"Zhao X, Zhang M, Tao R, Li W, Liao W, Philips W (2022) Cross-domain classification of multisource remote sensing data using fractional fusion and spatial-spectral domain adaptation. IEEE J Selected Topics Appl Earth Observ Remote Sens 15:5721\u20135733","journal-title":"IEEE J Selected Topics Appl Earth Observ Remote Sens"},{"key":"7126_CR4","doi-asserted-by":"crossref","unstructured":"Zhang M, Zhao X, Li W, Zhang Y, Tao R, Du Q (2023) Cross-scene joint classification of multisource data with multilevel domain adaption network. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2023.3262599"},{"issue":"2","key":"7126_CR5","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1109\/LRA.2023.3342555","volume":"9","author":"Y Wang","year":"2024","unstructured":"Wang Y, Bu S, Chen L, Dong Y, Li K, Cao X, Li K, Jin J (2024) Hybridfusion: Lidar and vision cross-source point cloud fusion. IEEE Robot Autom Lett 9(2):1139\u20131146","journal-title":"IEEE Robot Autom Lett"},{"key":"7126_CR6","doi-asserted-by":"crossref","unstructured":"Papadopoulos S, Anastassopoulos V, Koukiou G (2024) Pixel-level decision fusion for land cover classification using polsar data and local pattern differences. Electronics","DOI":"10.3390\/electronics13193846"},{"key":"7126_CR7","doi-asserted-by":"crossref","unstructured":"Papadopoulos S, Koukiou G, Anastassopoulos V (2024) Correlated decision fusion accompanied with quality information on a multi-band pixel basis for land cover classification. J Imaging 10(4)","DOI":"10.3390\/jimaging10040091"},{"key":"7126_CR8","doi-asserted-by":"crossref","unstructured":"Karachristos K, Koukiou G, Anastassopoulos V (2024) Fusion of coherent and non-coherent pol-sar features for land cover classification. Electronics 13(3)","DOI":"10.3390\/electronics13030634"},{"key":"7126_CR9","doi-asserted-by":"crossref","unstructured":"Papadopoulos S, Koukiou G, Anastassopoulos V (2024) Decision fusion at pixel level of multi-band data for land cover classification\u2014a review. J Imag 10","DOI":"10.3390\/jimaging10010015"},{"key":"7126_CR10","first-page":"1","volume":"63","author":"T Zhang","year":"2025","unstructured":"Zhang T, Chen Y, Zhu R, Wilson JP, Song J, Chen R, Liu L, Bao L (2025) An intelligent learning reconfiguration model based on optimized transformer and multisource features (tmsfs) for high-precision insar dem void filling. IEEE Trans Geosci Remote Sens 63:1\u201318","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"7126_CR11","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","volume":"5","author":"P Ghamisi","year":"2017","unstructured":"Ghamisi P, Yokoya N, Li J, Liao W, Liu S, Plaza J, Rasti B, Plaza A (2017) Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art. IEEE Geosci Remote Sens Mag 5(4):37\u201378","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"7126_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-020-3084-1","volume":"64","author":"Y Gu","year":"2021","unstructured":"Gu Y, Liu T, Gao G, Ren G, Ma Y, Chanussot J, Jia X (2021) Multimodal hyperspectral remote sensing: An overview and perspective. Sci China Inf Sci 64:1\u201324","journal-title":"Sci China Inf Sci"},{"key":"7126_CR13","doi-asserted-by":"crossref","unstructured":"Samiappan S, Dabbiru L, Moorhead R (2016) Fusion of hyperspectral and lidar data using random feature selection and morphological attribute profiles. In: 2016 8th Workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS), IEEE, pp 1\u20134","DOI":"10.1109\/WHISPERS.2016.8071662"},{"issue":"7","key":"7126_CR14","doi-asserted-by":"publisher","first-page":"3997","DOI":"10.1109\/TGRS.2017.2686450","volume":"55","author":"B Rasti","year":"2017","unstructured":"Rasti B, Ghamisi P, Gloaguen R (2017) Hyperspectral and lidar fusion using extinction profiles and total variation component analysis. IEEE Trans Geosci Remote Sens 55(7):3997\u20134007","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"7126_CR15","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1109\/TGRS.2008.916480","volume":"46","author":"M Dalponte","year":"2008","unstructured":"Dalponte M, Bruzzone L, Gianelle D (2008) Fusion of hyperspectral and lidar remote sensing data for classification of complex forest areas. IEEE Trans Geosci Remote Sens 46(5):1416\u20131427","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"7126_CR16","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/TGRS.2020.2996599","volume":"59","author":"S Jia","year":"2020","unstructured":"Jia S, Zhan Z, Zhang M, Xu M, Huang Q, Zhou J, Jia X (2020) Multiple feature-based superpixel-level decision fusion for hyperspectral and lidar data classification. IEEE Trans Geosci Remote Sens 59(2):1437\u20131452","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR17","doi-asserted-by":"publisher","first-page":"17099","DOI":"10.1109\/JSTARS.2024.3452494","volume":"17","author":"Z Li","year":"2024","unstructured":"Li Z, Wang Y, Wang L, Guo F, Yang Y, Wei J (2024) Pseudolabeling contrastive learning for semisupervised hyperspectral and lidar data classification. IEEE J Selected Topics Appl Earth Observations Remote Sens 17:17099\u201317116","journal-title":"IEEE J Selected Topics Appl Earth Observations Remote Sens"},{"key":"7126_CR18","first-page":"1","volume":"62","author":"W Ma","year":"2024","unstructured":"Ma W, Zhang H, Ma M, Chen C, Hou B (2024) Issp-net: An interactive spatial-spectral perception network for multimodal classification. IEEE Trans Geosci Remote Sens 62:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"7126_CR19","doi-asserted-by":"publisher","first-page":"4801","DOI":"10.1109\/TCSVT.2025.3525734","volume":"35","author":"X Wang","year":"2025","unstructured":"Wang X, Song L, Feng Y, Zhu J (2025) S3f2net: Spatial-spectral-structural feature fusion network for hyperspectral image and lidar data classification. IEEE Trans Circuits Syst Video Technol 35(5):4801\u20134815","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7126_CR20","first-page":"1","volume":"63","author":"F Liu","year":"2025","unstructured":"Liu F, Yao L, Zhang C, Wu T, Zhang X, Jiang X, Zhou J (2025) Boost uav-based object detection via scale-invariant feature disentanglement and adversarial learning. IEEE Trans Geosci Remote Sens 63:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR21","doi-asserted-by":"crossref","unstructured":"Yao L, Liu F, Chen D, Zhang C, Wang Y, Chen Z, Xu W, Di S, Zheng Y (2025) Remotesam: Towards segment anything for earth observation. In: Proceedings of the 33rd ACM international conference on multimedia","DOI":"10.1145\/3746027.3754950"},{"key":"7126_CR22","doi-asserted-by":"crossref","unstructured":"Yao L, Liu F, Xu S, Zhang C, Di S, Ma X, Jiang J, Wang Z, Zhou J (2025) Uemm-air: Enable uavs to undertake more multi-modal tasks, pp 12792\u201312798","DOI":"10.1145\/3746027.3758220"},{"key":"7126_CR23","doi-asserted-by":"crossref","unstructured":"Morchhale S, Pauca VP, Plemmons RJ, Torgersen TC (2016) Classification of pixel-level fused hyperspectral and lidar data using deep convolutional neural networks. In: 2016 8th Workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS), IEEE, pp 1\u20135","DOI":"10.1109\/WHISPERS.2016.8071715"},{"issue":"7","key":"7126_CR24","doi-asserted-by":"publisher","first-page":"4939","DOI":"10.1109\/TGRS.2020.2969024","volume":"58","author":"R Hang","year":"2020","unstructured":"Hang R, Li Z, Ghamisi P, Hong D, Xia G, Liu Q (2020) Classification of hyperspectral and lidar data using coupled cnns. IEEE Trans Geosci Remote Sens 58(7):4939\u20134950","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR25","first-page":"1","volume":"19","author":"T Zhang","year":"2021","unstructured":"Zhang T, Xiao S, Dong W, Qu J, Yang Y (2021) A mutual guidance attention-based multi-level fusion network for hyperspectral and lidar classification. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7126_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3040277","volume":"60","author":"X Wu","year":"2021","unstructured":"Wu X, Hong D, Chanussot J (2021) Convolutional neural networks for multimodal remote sensing data classification. IEEE Trans Geosci Remote Sens 60:1\u201310","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR27","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1109\/TCYB.2022.3169773","volume":"53","author":"M Zhang","year":"2023","unstructured":"Zhang M, Li W, Zhang Y, Tao R, Du Q (2023) Hyperspectral and lidar data classification based on structural optimization transmission. IEEE Trans Cybernet 53:3153\u20133164","journal-title":"IEEE Trans Cybernet"},{"key":"7126_CR28","first-page":"1","volume":"61","author":"G Zhao","year":"2022","unstructured":"Zhao G, Ye Q, Sun L, Wu Z, Pan C, Jeon B (2022) Joint classification of hyperspectral and lidar data using a hierarchical cnn and transformer. IEEE Trans Geosci Remote Sens 61:1\u201316","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR29","first-page":"1","volume":"61","author":"S Jia","year":"2023","unstructured":"Jia S, Zhou X, Jiang S, He R (2023) Collaborative contrastive learning for hyperspectral and lidar classification. IEEE Trans Geosci Remote Sens 61:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR30","first-page":"1","volume":"61","author":"Y Feng","year":"2023","unstructured":"Feng Y, Song L, Wang L, Wang X (2023) Dshfnet: Dynamic scale hierarchical fusion network based on multiattention for hyperspectral image and lidar data classification. IEEE Trans Geosci Remote Sens 61:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR31","doi-asserted-by":"crossref","unstructured":"Qu J, Zhang L, Dong W, Li N, Li Y (2024) Shared-private decoupling-based multilevel feature alignment semi-supervised learning for hsi and lidar classification. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2024.3492499"},{"key":"7126_CR32","first-page":"1","volume":"21","author":"X Wang","year":"2024","unstructured":"Wang X, Zhu J, Feng Y, Wang L (2024) Ms2canet: Multiscale spatial-spectral cross-modal attention network for hyperspectral image and lidar classification. IEEE Geosci Remote Sens Lett 21:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7126_CR33","doi-asserted-by":"crossref","unstructured":"Gao H, Feng H, Zhang Y, Fei S, Sheng R, Xu S, Zhang B (2024) Interactive enhanced network based on multihead self-attention and graph convolution for classification of hyperspectral and lidar data. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2024.3467674"},{"key":"7126_CR34","doi-asserted-by":"crossref","unstructured":"Lu Y, Yu W, Wei X, Huang J (2024) Am 2 cfn: Assimilation modality mapping guided crossmodal fusion network for hsi and lidar data joint classification. IEEE Geosci Remote Sens Lett","DOI":"10.1109\/LGRS.2024.3514179"},{"key":"7126_CR35","first-page":"1","volume":"63","author":"Y Kong","year":"2025","unstructured":"Kong Y, Yu S, Cheng Y, Philip Chen CL, Wang X (2025) Joint classification of hyperspectral images and lidar data based on candidate pseudo labels pruning and dual mixture of experts. IEEE Trans Geosci Remote Sens 63:1\u201312","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR36","doi-asserted-by":"crossref","unstructured":"Fan C-M, Liu T-J, Liu K-H (2022) Half wavelet attention on m-net+ for low-light image enhancement. In: 2022 IEEE International conference on image processing (ICIP), IEEE, pp 3878\u20133882","DOI":"10.1109\/ICIP46576.2022.9897503"},{"key":"7126_CR37","doi-asserted-by":"crossref","unstructured":"Zhao C, Cai W, Dong C, Hu C (2024) Wavelet-based fourier information interaction with frequency diffusion adjustment for underwater image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8281\u20138291","DOI":"10.1109\/CVPR52733.2024.00791"},{"key":"7126_CR38","doi-asserted-by":"crossref","unstructured":"Wang W, Yang T, Wang X (2024) From spatial to frequency domain: A pure frequency domain fdnet model for the classification of remote sensing images. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2024.3438942"},{"key":"7126_CR39","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhang C, Liu S, Shi Z, Pan B (2024) Three-dimensional frequency domain transform network for cross-scene hyperspectral image classification. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2024.3491159"},{"key":"7126_CR40","doi-asserted-by":"crossref","unstructured":"Yu C, Li H, Hu Y, Zhang Q, Song M, Wang Y (2024) Frequency-temporal attention network for remote sensing imagery change detection. IEEE Geosci Remote Sens Lett","DOI":"10.1109\/LGRS.2024.3477991"},{"key":"7126_CR41","doi-asserted-by":"crossref","unstructured":"Zhuang P, Zhang X, Wang H, Zhang T, Liu L, Li J (2025) Fahm: Frequency-aware hierarchical mamba for hyperspectral image classification. IEEE J Selected Topics Appl Earth Observ Remote Sens","DOI":"10.1109\/JSTARS.2025.3539791"},{"key":"7126_CR42","first-page":"1","volume":"62","author":"K Ni","year":"2024","unstructured":"Ni K, Wang D, Zhao G, Zheng Z, Wang P (2024) Hyperspectral and lidar classification via frequency domain-based network. IEEE Trans Geosci Remote Sens 62:1\u201317","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR43","first-page":"1","volume":"63","author":"Q Song","year":"2025","unstructured":"Song Q, Mo F, Ding K, Xiao L, Dian R, Kang X, Li S (2025) Mcfnet: Multiscale cross-domain fusion network for hsi and lidar data joint classification. IEEE Trans Geosci Remote Sens 63:1\u201312","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR44","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A.N, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"7126_CR45","unstructured":"Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. Adv Neural Inf Process Syst 27"},{"issue":"7","key":"7126_CR46","doi-asserted-by":"publisher","first-page":"3691","DOI":"10.1109\/TNNLS.2021.3113342","volume":"34","author":"X Chen","year":"2021","unstructured":"Chen X, Weng J, Deng X, Luo W, Lan Y, Tian Q (2021) Feature distillation in deep attention network against adversarial examples. IEEE Trans Neural Netw Learn Syst 34(7):3691\u20133705","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"10","key":"7126_CR47","doi-asserted-by":"publisher","first-page":"7719","DOI":"10.1109\/TNNLS.2022.3146004","volume":"34","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Chen Z, Li P, Song H, Chen CP, Sheng B (2022) Fsad-net: feedback spatial attention dehazing network. IEEE Trans Neural Netw Learn Syst 34(10):7719\u20137733","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7126_CR48","first-page":"1","volume":"60","author":"X Wang","year":"2022","unstructured":"Wang X, Wang X, Zhao K, Zhao X, Song C (2022) Fsl-unet: Full-scale linked unet with spatial-spectral joint perceptual attention for hyperspectral and multispectral image fusion. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR49","first-page":"1","volume":"19","author":"R Song","year":"2022","unstructured":"Song R, Ni W, Cheng W, Wang X (2022) Csanet: Cross-temporal interaction symmetric attention network for hyperspectral image change detection. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7126_CR50","first-page":"1","volume":"20","author":"X Wang","year":"2023","unstructured":"Wang X, Ni W, Feng Y, Song L (2023) Agf$$^2$$net: Attention-guided feature fusion network for multitemporal hyperspectral image change detection. IEEE Geosci Remote Sens Lett 20:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7126_CR51","doi-asserted-by":"crossref","unstructured":"Gan Y, Xu M, Liu W, Cao R (2025) Enhanced spatial-spectral attention network for hyperspectral image unmixing. IEEE Geosci Remote Sens Lett","DOI":"10.1109\/LGRS.2025.3551729"},{"key":"7126_CR52","doi-asserted-by":"crossref","unstructured":"Wang M, Sun Y, Xiang J, Zhong Y (2024) Citnet: Convolution interaction transformer network for hyperspectral and lidar image classification. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2024.3477965"},{"key":"7126_CR53","doi-asserted-by":"crossref","unstructured":"Ma B, Mu C, Liu Y, He X, Haidarh M (2025) Rosenet: Rotation and similarity enhancement network for multimodal remote sensing image land cover classification. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2025.3561850"},{"key":"7126_CR54","doi-asserted-by":"crossref","unstructured":"Chen Y, Fan H, Xu B, Yan Z, Kalantidis Y, Rohrbach M, Yan S, Feng J (2019) Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3435\u20133444","DOI":"10.1109\/ICCV.2019.00353"},{"issue":"3","key":"7126_CR55","doi-asserted-by":"publisher","first-page":"2430","DOI":"10.1109\/TGRS.2020.3005431","volume":"59","author":"X Tang","year":"2020","unstructured":"Tang X, Meng F, Zhang X, Cheung Y-M, Ma J, Liu F, Jiao L (2020) Hyperspectral image classification based on 3-d octave convolution with spatial-spectral attention network. IEEE Trans Geosci Remote Sens 59(3):2430\u20132447","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR56","doi-asserted-by":"crossref","unstructured":"Wu B, Wan A, Yue X, Jin P, Zhao S, Golmant N, Gholaminejad A, Gonzalez J, Keutzer K (2018) Shift: A zero flop, zero parameter alternative to spatial convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9127\u20139135","DOI":"10.1109\/CVPR.2018.00951"},{"key":"7126_CR57","first-page":"1","volume":"60","author":"Y Yang","year":"2022","unstructured":"Yang Y, Zhu D, Qu T, Wang Q, Ren F, Cheng C (2022) Single-stream cnn with learnable architecture for multisource remote sensing data. IEEE Trans Geosci Remote Sens 60:1\u201318","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR58","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.inffus.2022.12.020","volume":"93","author":"T Lu","year":"2023","unstructured":"Lu T, Ding K, Fu W, Li S, Guo A (2023) Coupled adversarial learning for fusion classification of hyperspectral and lidar data. Inf Fusion 93:118\u2013131","journal-title":"Inf Fusion"},{"key":"7126_CR59","first-page":"1","volume":"61","author":"SK Roy","year":"2023","unstructured":"Roy SK, Deria A, Hong D, Rasti B, Plaza A, Chanussot J (2023) Multimodal fusion transformer for remote sensing image classification. IEEE Trans Geosci Remote Sens 61:1\u201320","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125145","volume":"258","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Gao H, Zhou J, Zhang C, Ghamisi P, Xu S, Li C, Zhang B (2024) A cross-modal feature aggregation and enhancement network for hyperspectral and lidar joint classification. Expert Syst Appl 258:125145","journal-title":"Expert Syst Appl"},{"issue":"5","key":"7126_CR61","doi-asserted-by":"publisher","first-page":"4340","DOI":"10.1109\/TGRS.2020.3016820","volume":"59","author":"D Hong","year":"2020","unstructured":"Hong D, Gao L, Yokoya N, Yao J, Chanussot J, Du Q, Zhang B (2020) More diverse means better: Multimodal deep learning meets remote-sensing imagery classification. IEEE Trans Geosci Remote Sens 59(5):4340\u20134354","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7126_CR62","first-page":"1","volume":"19","author":"D Hong","year":"2020","unstructured":"Hong D, Gao L, Hang R, Zhang B, Chanussot J (2020) Deep encoder-decoder networks for classification of hyperspectral and lidar data. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07126-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07126-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07126-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:13:39Z","timestamp":1774934019000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07126-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":62,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["7126"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07126-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"30 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"105"}}