{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T20:43:54Z","timestamp":1782247434756,"version":"3.54.5"},"reference-count":59,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012130","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["No. 202000190U1002"],"award-info":[{"award-number":["No. 202000190U1002"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012130","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["20JY029"],"award-info":[{"award-number":["20JY029"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Scientific Research Plan of Education Department of Shaanxi Province, China","award":["No. 202000190U1002"],"award-info":[{"award-number":["No. 202000190U1002"]}]},{"name":"Key Scientific Research Plan of Education Department of Shaanxi Province, China","award":["20JY029"],"award-info":[{"award-number":["20JY029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detection and ranging (LiDAR) and hyperspectral imaging (HSI), have recognized the importance of incorporating multiple spatial scales. However, effectively capturing both long-range global correlations and short-range local features simultaneously on different scales remains a challenge, particularly in large-scale, complex ground scenes. To address this limitation, we propose a multi-scale graph encoder\u2013decoder network (MGEN) for multi-modal data classification. The MGEN adopts a graph model that maintains global sample correlations to fuse multi-scale features, enabling simultaneous extraction of local and global information. The graph encoder maps multi-modal data from different scales to the graph space and completes feature extraction in the graph space. The graph decoder maps the features of multiple scales back to the original data space and completes multi-scale feature fusion and classification. Experimental results on three HSI-LiDAR datasets demonstrate that the proposed MGEN achieves considerable classification accuracies and outperforms state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs16203912","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:49:22Z","timestamp":1729507762000},"page":"3912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Remote Sensing LiDAR and Hyperspectral Classification with Multi-Scale Graph Encoder\u2013Decoder Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1543-161X","authenticated-orcid":false,"given":"Fang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Optoelectronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-9061","authenticated-orcid":false,"given":"Xingqian","family":"Du","sequence":"additional","affiliation":[{"name":"China Academy of Space Technology (Xi\u2019an), Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiguang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"},{"name":"Xi\u2019an Institute of Optics and Precision Mechanics of CAS, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xi\u2019an Space Sensor Optical Technology Engineering Research Center, Xi\u2019an 710119, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary and Innovation Research, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, H., Zheng, T., Liu, Y., Zhang, Z., Xue, C., and Li, J. (2024). A joint convolutional cross ViT network for hyperspectral and light detection and ranging fusion classification. Remote Sens., 16.","DOI":"10.3390\/rs16030489"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, G., Chen, J., Mo, L., Wu, P., and Yi, X. (2024). Border-Enhanced Triple Attention Mechanism for High-Resolution Remote Sensing Images and Application to Land Cover Classification. Remote Sens., 16.","DOI":"10.3390\/rs16152814"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5508605","DOI":"10.1109\/LGRS.2024.3432604","article-title":"Orientational Clustering Learning for Open-Set Hyperspectral Image Classification","volume":"21","author":"Xu","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, Y., Jiang, S., Liu, Y., and Mu, C. (2024). Spatial Feature Enhancement and Attention-Guided Bidirectional Sequential Spectral Feature Extraction for Hyperspectral Image Classification. Remote Sens., 16.","DOI":"10.3390\/rs16173124"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.1109\/TCYB.2022.3169773","article-title":"Hyperspectral and LiDAR Data Classification Based on Structural Optimization Transmission","volume":"53","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5527116","DOI":"10.1109\/TGRS.2023.3322558","article-title":"Pseudo-Label-Based Unreliable Sample Learning for Semi-Supervised Hyperspectral Image Classification","volume":"61","author":"Yao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Chen, Y., Wang, Y., Wang, X., Wang, X., and Xiang, Z. (2024). DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image. Remote Sens., 16.","DOI":"10.3390\/rs16163002"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, A., Dai, S., Wu, H., and Iwahori, Y. (2024). Multimodal Semantic Collaborative Classification for Hyperspectral Images and LiDAR Data. Remote Sens., 16.","DOI":"10.3390\/rs16163082"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, R., Sun, L., and Zheng, Y. (2024). Multi-Feature, Cross Attention-Induced Transformer Network for Hyperspectral and LiDAR Data Classification. Remote Sens., 16.","DOI":"10.3390\/rs16152775"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1109\/TGRS.2020.3016820","article-title":"More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1049\/cit2.12208","article-title":"Scale-wise interaction fusion and knowledge distillation network for aerial scene recognition","volume":"8","author":"Ning","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_12","first-page":"5505614","article-title":"HyperMLP: Superpixel Prior and Feature Aggregated Perceptron Networks for Hyperspectral and Lidar Hybrid Classification","volume":"62","author":"Li","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bai, L., Li, Y., Cen, M., and Hu, F. (2021). 3D Instance Segmentation and Object Detection Framework Based on the Fusion of Lidar Remote Sensing and Optical Image Sensing. Remote Sens., 13.","DOI":"10.3390\/rs13163288"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhou, G., Xie, J., Fu, B., You, H., Chen, J., Shi, X., and Zhou, B. (2023). An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment. Remote Sens., 15.","DOI":"10.3390\/rs15092432"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10062","DOI":"10.1109\/TGRS.2020.3047130","article-title":"Multisource Remote Sensing Data Classification With Graph Fusion Network","volume":"59","author":"Du","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5503215","DOI":"10.1109\/TGRS.2023.3346935","article-title":"Spectral\u2013Spatial\u2013Language Fusion Network for Hyperspectral, LiDAR, and Text Data Classification","volume":"62","author":"Cao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","first-page":"5500114","article-title":"Sal2RN: A Spatial\u2013Spectral Salient Reinforcement Network for Hyperspectral and LiDAR Data Fusion Classification","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chroni, A., Vasilakos, C., Christaki, M., and Soulakellis, N. (2024). Fusing Multispectral and LiDAR Data for CNN-Based Semantic Segmentation in Semi-Arid Mediterranean Environments: Land Cover Classification and Analysis. Remote Sens., 16.","DOI":"10.3390\/rs16152729"},{"key":"ref_19","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":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wang, X., Wang, H., and Cheng, Y. (2024). Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification. Remote Sens., 16.","DOI":"10.20944\/preprints202402.1296.v1"},{"key":"ref_21","first-page":"5509516","article-title":"Multisource Feature Embedding and Interaction Fusion Network for Coastal Wetland Classification With Hyperspectral and LiDAR Data","volume":"62","author":"Guo","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5704513","DOI":"10.1109\/TGRS.2023.3321057","article-title":"Hashing-Based Deep Metric Learning for the Classification of Hyperspectral and LiDAR Data","volume":"61","author":"Song","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"5501505","article-title":"MS2CANet: Multiscale Spatial\u2013Spectral Cross-Modal Attention Network for Hyperspectral Image and LiDAR Classification","volume":"21","author":"Wang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7355","DOI":"10.1109\/TGRS.2020.2982064","article-title":"Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture","volume":"58","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5522514","DOI":"10.1109\/TGRS.2023.3311535","article-title":"DSHFNet: Dynamic Scale Hierarchical Fusion Network Based on Multiattention for Hyperspectral Image and LiDAR Data Classification","volume":"61","author":"Feng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"5501816","article-title":"Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"1","article-title":"Multiscale Deep Learning Network With Self-Calibrated Convolution for Hyperspectral and LiDAR Data Collaborative Classification","volume":"60","author":"Xue","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5531317","DOI":"10.1109\/TGRS.2023.3331486","article-title":"AMSSE-Net: Adaptive Multiscale Spatial\u2013Spectral Enhancement Network for Classification of Hyperspectral and LiDAR Data","volume":"61","author":"Gao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mohla, S., Pande, S., Banerjee, B., and Chaudhuri, S. (2020, January 14\u201319). Fusatnet: Dual attention based spectrospatial multimodal fusion network for hyperspectral and lidar classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.21203\/rs.3.rs-32802\/v1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Wang, C., Zhang, H., Wang, H., Xi, X., Yang, Z., and Du, M. (2024). TCPSNet: Transformer and Cross-Pseudo-Siamese Learning Network for Classification of Multi-Source Remote Sensing Images. Remote Sens., 16.","DOI":"10.3390\/rs16173120"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, M., Sun, Y., Xiang, J., Sun, R., and Zhong, Y. (2024). Joint Classification of Hyperspectral and LiDAR Data Based on Adaptive Gating Mechanism and Learnable Transformer. Remote Sens., 16.","DOI":"10.3390\/rs16061080"},{"key":"ref_32","first-page":"5503605","article-title":"Dual-Branch Feature Fusion Network Based Cross-Modal Enhanced CNN and Transformer for Hyperspectral and LiDAR Classification","volume":"21","author":"Wang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","first-page":"5500716","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."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5505315","DOI":"10.1109\/TGRS.2024.3353775","article-title":"Joint Classification of Hyperspectral and LiDAR Data Using Height Information Guided Hierarchical Fusion-and-Separation Network","volume":"62","author":"Song","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3472455","article-title":"LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification","volume":"62","author":"Yang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/JSTARS.2022.3232995","article-title":"Local Information Interaction Transformer for Hyperspectral and LiDAR Data Classification","volume":"16","author":"Zhang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5470","DOI":"10.1109\/JSTARS.2024.3366614","article-title":"MHST: Multiscale Head Selection Transformer for Hyperspectral and LiDAR Classification","volume":"17","author":"Ni","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","first-page":"2100116","article-title":"Multiscale 3-D\u20132-D Mixed CNN and Lightweight Attention-Free Transformer for Hyperspectral and LiDAR Classification","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"111190","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":"ref_40","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.1109\/TNNLS.2022.3189994","article-title":"Fractional Fourier image transformer for multimodal remote sensing data classification","volume":"35","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5528613","DOI":"10.1109\/TGRS.2023.3325818","article-title":"Multiview Feature Learning and Multilevel Information Fusion for Joint Classification of Hyperspectral and LiDAR Data","volume":"61","author":"Feng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"123587","DOI":"10.1016\/j.eswa.2024.123587","article-title":"A novel graph-attention based multimodal fusion network for joint classification of hyperspectral image and LiDAR data","volume":"249","author":"Cai","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/TGRS.2020.2994205","article-title":"Hyperspectral image classification with context-aware dynamic graph convolutional network","volume":"59","author":"Wan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","first-page":"8002005","article-title":"Remote sensing image classification based on a cross-attention mechanism and graph convolution","volume":"19","author":"Cai","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/LGRS.2020.2966239","article-title":"Semisupervised classification for hyperspectral images using graph attention networks","volume":"18","author":"Sha","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5535815","DOI":"10.1109\/TGRS.2022.3199467","article-title":"Multiscale short and long range graph convolutional network for hyperspectral image classification","volume":"60","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A Comprehensive Survey on Graph Neural Networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","article-title":"Wavelets on graphs via spectral graph theory","volume":"30","author":"Hammond","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Qi, X., Wang, T., and Liu, J. (2017, January 8\u201310). Comparison of Support Vector Machine and Softmax Classifiers in Computer Vision. Proceedings of the 2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Harbin, China.","DOI":"10.1109\/ICMCCE.2017.49"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3011","DOI":"10.1109\/JSTARS.2016.2634863","article-title":"Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network","volume":"10","author":"Ghamisi","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","unstructured":"Gader, P., Zare, A., Close, R., Aitken, J., and Tuell, G. (2013). MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set, University Florida. Technical Report REP-2013-570."},{"key":"ref_53","unstructured":"Du, X., and Zare, A. (2017). Scene Label Ground Truth Map for MUUFL Gulfport Data Set, University of Florida. Technical Report."},{"key":"ref_54","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":"ref_55","doi-asserted-by":"crossref","unstructured":"Cahill, N.D., Czaja, W., and Messinger, D.W. (2014, January 8\u201321). Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery. Proceedings of the Defense + Security Symposium, San Jose, CA, USA.","DOI":"10.1117\/12.2050651"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going Deeper with Contextual CNN for Hyperspectral Image Classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4420","DOI":"10.1109\/TGRS.2018.2818945","article-title":"3-D Deep Learning Approach for Remote Sensing Image Classification","volume":"56","author":"Benoit","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","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":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhao, X., Tao, R., and Li, W. (2019, January 12\u201317). Multisource Remote Sensing Data Classification Using Deep Hierarchical Random Walk Networks. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683032"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3912\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:17:39Z","timestamp":1760113059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":59,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16203912"],"URL":"https:\/\/doi.org\/10.3390\/rs16203912","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,21]]}}}