{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:38:58Z","timestamp":1774449538658,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T00:00:00Z","timestamp":1731974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176217"],"award-info":[{"award-number":["62176217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KCXTD2022-3"],"award-info":[{"award-number":["KCXTD2022-3"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023YFG0028"],"award-info":[{"award-number":["2023YFG0028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023YFS0431"],"award-info":[{"award-number":["2023YFS0431"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["R23CGZH0001"],"award-info":[{"award-number":["R23CGZH0001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023ZYD0148"],"award-info":[{"award-number":["2023ZYD0148"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023YFG0130"],"award-info":[{"award-number":["2023YFG0130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["R22ZYZF0004"],"award-info":[{"award-number":["R22ZYZF0004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Team Funds of China West Normal University","award":["62176217"],"award-info":[{"award-number":["62176217"]}]},{"name":"Innovation Team Funds of China West Normal University","award":["KCXTD2022-3"],"award-info":[{"award-number":["KCXTD2022-3"]}]},{"name":"Innovation Team Funds of China West Normal University","award":["2023YFG0028"],"award-info":[{"award-number":["2023YFG0028"]}]},{"name":"Innovation Team Funds of China West Normal University","award":["2023YFS0431"],"award-info":[{"award-number":["2023YFS0431"]}]},{"name":"Innovation Team Funds of China West Normal University","award":["R23CGZH0001"],"award-info":[{"award-number":["R23CGZH0001"]}]},{"name":"Innovation Team Funds of China West Normal University","award":["2023ZYD0148"],"award-info":[{"award-number":["2023ZYD0148"]}]},{"name":"Innovation Team Funds of China West Normal University","award":["2023YFG0130"],"award-info":[{"award-number":["2023YFG0130"]}]},{"name":"Innovation Team Funds of China West Normal University","award":["R22ZYZF0004"],"award-info":[{"award-number":["R22ZYZF0004"]}]},{"name":"Sichuan Science and Technology Program of China","award":["62176217"],"award-info":[{"award-number":["62176217"]}]},{"name":"Sichuan Science and Technology Program of China","award":["KCXTD2022-3"],"award-info":[{"award-number":["KCXTD2022-3"]}]},{"name":"Sichuan Science and Technology Program of China","award":["2023YFG0028"],"award-info":[{"award-number":["2023YFG0028"]}]},{"name":"Sichuan Science and Technology Program of China","award":["2023YFS0431"],"award-info":[{"award-number":["2023YFS0431"]}]},{"name":"Sichuan Science and Technology Program of China","award":["R23CGZH0001"],"award-info":[{"award-number":["R23CGZH0001"]}]},{"name":"Sichuan Science and Technology Program of China","award":["2023ZYD0148"],"award-info":[{"award-number":["2023ZYD0148"]}]},{"name":"Sichuan Science and Technology Program of China","award":["2023YFG0130"],"award-info":[{"award-number":["2023YFG0130"]}]},{"name":"Sichuan Science and Technology Program of China","award":["R22ZYZF0004"],"award-info":[{"award-number":["R22ZYZF0004"]}]},{"name":"A Ba Achievements Transformation Program","award":["62176217"],"award-info":[{"award-number":["62176217"]}]},{"name":"A Ba Achievements Transformation Program","award":["KCXTD2022-3"],"award-info":[{"award-number":["KCXTD2022-3"]}]},{"name":"A Ba Achievements Transformation Program","award":["2023YFG0028"],"award-info":[{"award-number":["2023YFG0028"]}]},{"name":"A Ba Achievements Transformation Program","award":["2023YFS0431"],"award-info":[{"award-number":["2023YFS0431"]}]},{"name":"A Ba Achievements Transformation Program","award":["R23CGZH0001"],"award-info":[{"award-number":["R23CGZH0001"]}]},{"name":"A Ba Achievements Transformation Program","award":["2023ZYD0148"],"award-info":[{"award-number":["2023ZYD0148"]}]},{"name":"A Ba Achievements Transformation Program","award":["2023YFG0130"],"award-info":[{"award-number":["2023YFG0130"]}]},{"name":"A Ba Achievements Transformation Program","award":["R22ZYZF0004"],"award-info":[{"award-number":["R22ZYZF0004"]}]},{"name":"Sichuan Science and Technology Program of China","award":["62176217"],"award-info":[{"award-number":["62176217"]}]},{"name":"Sichuan Science and Technology Program of China","award":["KCXTD2022-3"],"award-info":[{"award-number":["KCXTD2022-3"]}]},{"name":"Sichuan Science and Technology Program of China","award":["2023YFG0028"],"award-info":[{"award-number":["2023YFG0028"]}]},{"name":"Sichuan Science and Technology Program of China","award":["2023YFS0431"],"award-info":[{"award-number":["2023YFS0431"]}]},{"name":"Sichuan Science and Technology Program of China","award":["R23CGZH0001"],"award-info":[{"award-number":["R23CGZH0001"]}]},{"name":"Sichuan Science and Technology Program of China","award":["2023ZYD0148"],"award-info":[{"award-number":["2023ZYD0148"]}]},{"name":"Sichuan Science and Technology Program of China","award":["2023YFG0130"],"award-info":[{"award-number":["2023YFG0130"]}]},{"name":"Sichuan Science and Technology Program of China","award":["R22ZYZF0004"],"award-info":[{"award-number":["R22ZYZF0004"]}]},{"name":"Sichuan Province Transfer Payment Application and Development Program","award":["62176217"],"award-info":[{"award-number":["62176217"]}]},{"name":"Sichuan Province Transfer Payment Application and Development Program","award":["KCXTD2022-3"],"award-info":[{"award-number":["KCXTD2022-3"]}]},{"name":"Sichuan Province Transfer Payment Application and Development Program","award":["2023YFG0028"],"award-info":[{"award-number":["2023YFG0028"]}]},{"name":"Sichuan Province Transfer Payment Application and Development Program","award":["2023YFS0431"],"award-info":[{"award-number":["2023YFS0431"]}]},{"name":"Sichuan Province Transfer Payment Application and Development Program","award":["R23CGZH0001"],"award-info":[{"award-number":["R23CGZH0001"]}]},{"name":"Sichuan Province Transfer Payment Application and Development Program","award":["2023ZYD0148"],"award-info":[{"award-number":["2023ZYD0148"]}]},{"name":"Sichuan Province Transfer Payment Application and Development Program","award":["2023YFG0130"],"award-info":[{"award-number":["2023YFG0130"]}]},{"name":"Sichuan Province Transfer Payment Application and Development Program","award":["R22ZYZF0004"],"award-info":[{"award-number":["R22ZYZF0004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, multisource remote sensing image data can provide complementary information for the same feature, promoting its recognition. The effective utilization of remote sensing image data from various sources can enhance the extraction of image features and improve the accuracy of feature recognition. Hyperspectral remote sensing (HSI) data and light detection and ranging (LiDAR) data can provide complementary information from different perspectives and are frequently combined in feature identification tasks. However, the process of joint use suffers from data redundancy, low classification accuracy and high time complexity. To address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multiprobability decision fusion (PRDRMF) method for the combined classification of HSI and LiDAR data. First, the original HSI data and LiDAR data are downscaled via the principal component\u2013relative total variation (PRTV) method to remove redundant information. In the multifeature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. This is achieved by utilizing the local binary pattern (LBP) and extended multiattribute profile (EMAP) for the two types of data after dimensionality reduction. The four extracted features are subsequently input into the corresponding kernel\u2013extreme learning machine (KELM), which has a simple structure and good classification performance, to obtain four classification probability matrices (CPMs). Finally, the four CPMs are fused via a multiprobability decision fusion method to obtain the optimal classification results. Comparison experiments on four classical HSI and LiDAR datasets demonstrate that the method proposed in this paper achieves high classification performance while reducing the overall time complexity of the method.<\/jats:p>","DOI":"10.3390\/rs16224317","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T09:30:25Z","timestamp":1732008625000},"page":"4317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method"],"prefix":"10.3390","volume":"16","author":[{"given":"Tao","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science, China West Normal University, Nanchong 637002, China"}]},{"given":"Sizuo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, China West Normal University, Nanchong 637002, China"}]},{"given":"Luying","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"}]},{"given":"Huayue","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, China West Normal University, Nanchong 637002, China"},{"name":"Institute of Artificial Intelligence, China West Normal University, Nanchong 637002, China"},{"name":"Key Laboratory of Optimization Theory and Applications, China West Normal University, Nanchong 637002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4495-8299","authenticated-orcid":false,"given":"Bochuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science, China West Normal University, Nanchong 637002, China"},{"name":"Institute of Artificial Intelligence, China West Normal University, Nanchong 637002, China"},{"name":"Key Laboratory of Optimization Theory and Applications, China West Normal University, Nanchong 637002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6524-6760","authenticated-orcid":false,"given":"Wu","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"},{"name":"State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ding, Z., Liao, X., Su, F., and Fu, D. (2017). Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining. Remote Sens., 9.","DOI":"10.3390\/rs9020116"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"30754","DOI":"10.1109\/JIOT.2024.3412925","article-title":"Semi-supervised adaptive pseudo-label feature learning for hyperspectral image classification in internet of things","volume":"11","author":"Chen","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_3","first-page":"141","article-title":"Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes","volume":"2","author":"Simani","year":"2024","journal-title":"J. Data Sci. Intell. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, X., Zhao, H., Xu, J., Zhu, G., and Deng, W. (2024). APDPFL: Anti-Poisoning Attack Decentralized Privacy Enhanced Federated Learning Scheme for Flight Operation Data Sharing. IEEE Trans. Wirel. Commun., 1.","DOI":"10.1109\/TWC.2024.3479149"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/LGRS.2017.2687519","article-title":"Discriminative Graph-Based Fusion of HSI and LiDAR Data for Urban Area Classification","volume":"14","author":"Gu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"32804","DOI":"10.1109\/JIOT.2024.3409823","article-title":"Defect detection using shuffle Net-CA-SSD lightweight network for turbine blades in IoT","volume":"11","author":"Zhao","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"209","DOI":"10.2478\/jtim-2023-0143","article-title":"EV-Call 120: A new-generation emergency medical service system in China","volume":"12","author":"Xie","year":"2024","journal-title":"J. Transl. Intern. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, D., Li, Y., Hou, M., Liu, J., Zhao, Z., Guo, A., Zhao, H., and Deng, W. (2024). Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data. Struct. Health Monit.","DOI":"10.1177\/14759217241254121"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112252","DOI":"10.1016\/j.asoc.2024.112252","article-title":"Competitive swarm optimizer with dynamic multi-competitions and convergence accelerator for large-scale optimization problems","volume":"167","author":"Huang","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6354","DOI":"10.1109\/TGRS.2017.2726901","article-title":"Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis","volume":"55","author":"Rasti","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deng, W., Li, X., Xu, J., Li, W., Zhu, G., and Zhao, H. (2024). BFKD: Blockchain-Based Federated Knowledge Distillation for Aviation Internet of Things. IEEE Trans. Reliab., 1\u201314.","DOI":"10.1109\/TR.2024.3474710"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109237","DOI":"10.1016\/j.engappai.2024.109237","article-title":"A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system","volume":"137","author":"Ran","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22892","DOI":"10.1109\/JIOT.2024.3360432","article-title":"Few-shot cross-domain fault diagnosis of bearing driven by Task-supervised ANIL","volume":"11","author":"Shao","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111793","DOI":"10.1016\/j.knosys.2024.111793","article-title":"Adaptive weighted ensemble clustering via kernel learning and local information preservation","volume":"294","author":"Li","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"121557","DOI":"10.1016\/j.eswa.2023.121557","article-title":"Ensemble clustering via fusing global and local structure information","volume":"237","author":"Xu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10249","DOI":"10.1109\/TII.2024.3393550","article-title":"Class-Imbalanced Spatial\u2013Temporal Feature Learning for Blade Icing Recognition of Wind Turbine","volume":"20","author":"Wang","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5312","DOI":"10.1109\/TGRS.2015.2421051","article-title":"Novel MKL Model of Integrating LiDAR Data and MSI for Urban Area Classification","volume":"53","author":"Gu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, T., Wang, T., Chen, H., Zheng, B., and Deng, W. (2024). Cross-Hopping Graph Networks for Hyperspectral\u2013High Spatial Resolution (H2) Image Classification. Remote Sens., 16.","DOI":"10.3390\/rs16173155"},{"key":"ref_19","first-page":"1","article-title":"3D-STCNN: Spatiotemporal convolutional neural network based on EEG 3D features for detecting driving fatigue","volume":"2","author":"Peng","year":"2024","journal-title":"J. Data Sci. Intell. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, T., and Chen, T. (2023). Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network. Remote Sens., 15.","DOI":"10.3390\/rs15133402"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3445","DOI":"10.1109\/TITS.2023.3325989","article-title":"Selective Feature Fusion and Irregular-Aware Network for Pavement Crack Detection","volume":"25","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5510305","DOI":"10.1109\/LGRS.2023.3322711","article-title":"Hyperspectral and LiDAR Data Classification Using Spatial Context and De-Redundant Fusion Network","volume":"20","author":"Dong","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"032025","DOI":"10.1088\/1742-6596\/1368\/3\/032025","article-title":"Elimination of information redundancy of hyperspectral images using the \u201cwell-adapted\u201d basis method","volume":"1368","author":"Vasin","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1109\/TNNLS.2021.3105543","article-title":"Enhanced Deep Blind Hyperspectral Image Fusion","volume":"34","author":"Wang","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_25","first-page":"339","article-title":"Extracting spectral contrast in Lands at thematic mapper image data using selective principal component analysis","volume":"55","author":"Chavez","year":"1989","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","unstructured":"Switzer, P., and Green, A. (1984). Min\/Max. Autocorrelation Factors for Multivariate Spatial Imagery, Deptartment of Statistics, Stanford University."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/LGRS.2011.2172185","article-title":"Benediktsson Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles","volume":"9","author":"Licciardi","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1109\/TGRS.2017.2686450","article-title":"Hyperspectral and LiDAR fusion using extinction profiles and total variation component analysis","volume":"55","author":"Rasti","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/LGRS.2014.2350263","article-title":"Generalized graph-based fusion of hyperspectral and LiDAR data usingmorphological features","volume":"12","author":"Liao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local binary patterns and extreme learning machine for hyperspectral imagery classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"542","article-title":"Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis","volume":"8","author":"Villa","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett. Dec."},{"key":"ref_32","first-page":"2531215","article-title":"Automatic assessment method and device for depression symptom severity based on emotional facial expression and pupil-wave","volume":"73","author":"Li","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, H., Wang, L., Zhao, Z., and Deng, W. (2024). A new fault diagnosis approach using parameterized time-reassigned multisynchrosqueezing transform for rolling bearings. IEEE Trans. Reliab., 1\u201310.","DOI":"10.1109\/TR.2024.3371520"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"108638","DOI":"10.1016\/j.engappai.2024.108638","article-title":"A dual-time dual-population multi-objective evolutionary algorithm with application to the portfolio optimization problem","volume":"133","author":"Song","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"16693","DOI":"10.1109\/JIOT.2024.3354942","article-title":"IOFL: Intelligent-optimization-based federated learning for Non-IID data","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, T., Ren, R., Li, Y., and Liu, W. (2024). A Model-Based Reinforcement Learning Method with Conditional Variational Auto-Encoder. J. Data Sci. Intell. Syst.","DOI":"10.47852\/bonviewJDSIS42022432"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"117467","DOI":"10.1016\/j.oceaneng.2024.117467","article-title":"A study on ice resistance prediction based on deep learning data generation method","volume":"301","author":"Sun","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3352","DOI":"10.1109\/TITS.2023.3326281","article-title":"SAFENESS: A Semi-Supervised Transfer Learning Approach for Sea State Estimation Using Ship Motion Data","volume":"25","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5258","DOI":"10.1038\/s41467-023-40903-9","article-title":"Flight trajectory prediction enabled by time-frequency wavelet transform","volume":"14","author":"Zhang","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Duan, H. (2018). Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information. Remote Sens., 10.","DOI":"10.3390\/rs10030441"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","article-title":"An insight into extreme learning machines: Random neurons, random features and kernels","volume":"6","author":"Huang","year":"2014","journal-title":"Cogn. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11220-015-0126-z","article-title":"Spectral\u2013Spatial Hyperspectral Image Classification Based on KNN","volume":"17","author":"Huang","year":"2016","journal-title":"Sens. Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4939","DOI":"10.1109\/TGRS.2020.2969024","article-title":"Classification of hyperspectral and LiDAR data using coupled CNNs","volume":"58","author":"Hang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","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_45","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_46","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/2150704X.2013.805279","article-title":"Kernel-based extreme learning machine for remote-sensing image classification","volume":"4","author":"Pal","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1109\/TGRS.2008.916207","article-title":"Decision fusion with confidence-based weight assignment for hyperspectral target recognition","volume":"46","author":"Prasad","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/JSTARS.2013.2295313","article-title":"Gabor-filtering-based nearest regularized subspace for hyperspectral image classification","volume":"7","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4581","DOI":"10.1109\/TGRS.2018.2828029","article-title":"SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery","volume":"56","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"117611","DOI":"10.1016\/j.oceaneng.2024.117611","article-title":"Investigation of ice wedge bearing capacity based on an anisotropic beam analogy","volume":"302","author":"Li","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5728","DOI":"10.1109\/TCSS.2024.3393247","article-title":"Automatic diagnosis of depression based on facial expression information and deep convolutional neural network","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101983","DOI":"10.1016\/j.inffus.2023.101983","article-title":"Fuel consumption prediction for pre-departure flights using attention-based multi-modal fusion","volume":"101","author":"Lin","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_53","first-page":"1828","article-title":"FlightBERT: Binary Encoding Representation for Flight Trajectory Prediction","volume":"24","author":"Guo","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2366145.2366213","article-title":"Structure extraction from texture via relative total variation","volume":"31","author":"Li","year":"2012","journal-title":"ACM Trans. Graph."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5975","DOI":"10.1080\/01431161.2010.512425","article-title":"Extended profiles with morphological attribute filters for the analysis of hyperspectral data","volume":"31","author":"Mura","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5500205","DOI":"10.1109\/LGRS.2020.3017414","article-title":"Deep encoder-decoder networks for classification of hyperspectral and LiDAR data","volume":"19","author":"Hong","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep recurrent neural networks for hyperspectral image classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1109\/TIP.2018.2799324","article-title":"Hyperspectral image classification with Markov random fields and a convolutional neural network","volume":"27","author":"Cao","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_60","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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00054"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6504205","DOI":"10.1109\/LGRS.2021.3121028","article-title":"S2ENet: Spatial-spectral cross-modal enhancement network for classification of hyperspectral and LiDAR data","volume":"19","author":"Fang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_62","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_63","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_64","first-page":"5409218","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":"ref_65","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4317\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:35:19Z","timestamp":1760114119000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,19]]},"references-count":65,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224317"],"URL":"https:\/\/doi.org\/10.3390\/rs16224317","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,19]]}}}