12.1.4.8.1 Fusion, Hyperspectral and or Depth, LiDAR

Braun, A.C.[Andreas C.], Weidner, U.[Uwe], Jutzi, B.[Boris], Hinz, S.[Stefan],
Kernel Composition with the one-against-one Cascade for Integrating External Knowledge into SVM Classification,
PFG(2012), No. 4, 2012, pp. 371-384.
WWW Link. 1211
BibRef
Earlier:
Integrating external knowledge into SVM classification: Fusing hyperspectral and laserscanning data by kernel composition.,
HighRes11(xx-yy).
PDF File. 1106
BibRef

Braun, A.C.[Andreas Christian], Weidner, U.[Uwe], Hinz, S.[Stefan],
Support Vector Machines for Vegetation Classification A Revision,
PFG(2010), No. 4, 2010, pp. 273-281.
WWW Link. 1211
BibRef

Lee, J.[Juheon], Cai, X.H.[Xiao-Hao], Schonlieb, C.B., Coomes, D.A.,
Nonparametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes,
GeoRS(53), No. 11, November 2015, pp. 6073-6084.
IEEE DOI 1509
geophysical image processing BibRef

Brell, M., Rogass, C., Segl, K., Bookhagen, B., Guanter, L.,
Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data,
GeoRS(54), No. 6, June 2016, pp. 3460-3474.
IEEE DOI 1606
geophysical image processing BibRef

Demarchi, L.[Luca], Canters, F.[Frank], Cariou, C.[Claude], Licciardi, G.[Giorgio], Chan, J.C.W.[Jonathan Cheung-Wai],
Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping,
PandRS(87), No. 1, 2014, pp. 166-179.
Elsevier DOI 1402
Airborne high-resolution hyperspectral imagery BibRef

Priem, F.[Frederik], Canters, F.[Frank],
Synergistic Use of LiDAR and APEX Hyperspectral Data for High-Resolution Urban Land Cover Mapping,
RS(8), No. 10, 2016, pp. 787.
DOI Link 1609
BibRef

Brell, M., Segl, K., Guanter, L., Bookhagen, B.,
Hyperspectral and Lidar Intensity Data Fusion: A Framework for the Rigorous Correction of Illumination, Anisotropic Effects, and Cross Calibration,
GeoRS(55), No. 5, May 2017, pp. 2799-2810.
IEEE DOI 1705
geophysical image processing, hyperspectral imaging, image classification, image fusion, land use, optical radar, remote sensing by laser beam, vegetation, active sensor system, airborne lidar scanner, anisotropy effect, geometric accuracy, hyperspectral data, BibRef

Kandare, K.[Kaja], Dalponte, M.[Michele], Ørka, H.O.[Hans Ole], Frizzera, L.[Lorenzo], Næsset, E.[Erik],
Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Rasti, B.[Behnood], Ghamisi, P.[Pedram], Plaza, J., Plaza, A.,
Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis,
GeoRS(55), No. 11, November 2017, pp. 6354-6365.
IEEE DOI 1711
Data mining, Feature extraction, Gray-scale, Hyperspectral imaging, Laser radar, Extinction profiles (EPs), hyperspectral, light detection and ranging (LiDAR), sparse, and, low-rank, component, analysis, (SLRCA) BibRef

Rasti, B.[Behnood], Ghamisi, P.[Pedram], Ulfarsson, M.O.[Magnus O.],
Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Rasti, B., Ulfarsson, M.O., Sveinsson, J.R.,
Hyperspectral Feature Extraction Using Total Variation Component Analysis,
GeoRS(54), No. 12, December 2016, pp. 6976-6985.
IEEE DOI 1612
feature extraction BibRef

Rasti, B., Ghamisi, P., Gloaguen, R.,
Hyperspectral and LiDAR Fusion Using Extinction Profiles and Total Variation Component Analysis,
GeoRS(55), No. 7, July 2017, pp. 3997-4007.
IEEE DOI 1706
Data mining, Feature extraction, Hyperspectral imaging, Laser radar, Support vector machines, Extinction profiles (EPs), feature fusion, orthogonal total variation component analysis (OTVCA), random forest (RF), support, vector, machines, (SVMs) BibRef

Li, H.[Hao], Ghamisi, P.[Pedram], Soergel, U.[Uwe], Zhu, X.X.[Xiao Xiang],
Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Aytaylan, H.[Hakan], Yuksel, S.E.[Seniha Esen],
Fully-connected semantic segmentation of hyperspectral and LiDAR data,
IET-CV(13), No. 3, April 2019, pp. 285-293.
DOI Link 1904
BibRef

Brell, M.[Maximilian], Segl, K.[Karl], Guanter, L.[Luis], Bookhagen, B.[Bodo],
3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction,
PandRS(149), 2019, pp. 200-214.
Elsevier DOI 1903
Lidar, Multispectral point cloud, Laser return intensity, Unmixing, Sharpening, Imaging spectroscopy, In-flight, Semantic labeling BibRef

Li, Y.S.[Yun-Song], Ge, C.[Chiru], Sun, W.W.[Wei-Wei], Peng, J.T.[Jiang-Tao], Du, Q.[Qian], Wang, K.[Keyan],
Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Slawik, L.[Lukasz], Niedzielko, J.[Jan], Kania, A.[Adam], Piórkowski, H.[Hubert], Kopec, D.[Dominik],
Multiple Flights or Single Flight Instrument Fusion of Hyperspectral and ALS Data? A Comparison of their Performance for Vegetation Mapping,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
Airborne Laser System. BibRef

Xue, Z.H.[Zhao-Hui], Yang, S.[Sirui], Zhang, H.Y.[Hong-Yan], Du, P.J.[Pei-Jun],
Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Hang, R., Li, Z., Ghamisi, P., Hong, D., Xia, G., Liu, Q.,
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs,
GeoRS(58), No. 7, July 2020, pp. 4939-4950.
IEEE DOI 2006
Hyperspectral imaging, Laser radar, Feature extraction, Fuses, Data models, Convolutional neural networks (CNNs), parameter sharing BibRef

Zhao, X., Tao, R., Li, W., Li, H.C., Du, Q., Liao, W., Philips, W.,
Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture,
GeoRS(58), No. 10, October 2020, pp. 7355-7370.
IEEE DOI 2009
Feature extraction, Laser radar, Hyperspectral imaging, Convolution, Probability distribution, hierarchical random walk BibRef

Jia, S.[Sen], Zhan, Z.W.[Zhang-Wei], Zhang, M.[Meng], Xu, M.[Meng], Huang, Q.[Qiang], Zhou, J.[Jun], Jia, X.P.[Xiu-Ping],
Multiple Feature-Based Superpixel-Level Decision Fusion for Hyperspectral and LiDAR Data Classification,
GeoRS(59), No. 2, February 2021, pp. 1437-1452.
IEEE DOI 2101
Laser radar, Feature extraction, Hyperspectral imaging, Sensors, Data mining, Feature extraction, feature fusion, superpixel segmentation BibRef

Jia, S.[Sen], Zhang, M.[Meng], Xian, J.J.[Jun-Jian], Zhuang, J.Y.[Jia-Yue], Huang, Q.[Qiang],
Superpixel-Based Feature Extraction and Fusion Method for Hyperspectral and LiDAR Classification,
ICPR18(764-769)
IEEE DOI 1812
Feature extraction, Hyperspectral imaging, Laser radar, Wavelet domain, Entropy, Image segmentation BibRef

Tu, B.[Bing], Zhu, Y.[Yu], Zhou, C.[Chengle], Chen, S.Y.[Si-Yuan], Plaza, A.[Antonio],
Optimized Spatial Gradient Transfer for Hyperspectral-LiDAR Data Classification,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Decker, K.T.[Kevin T.], Borghetti, B.J.[Brett J.],
Composite Style Pixel and Point Convolution-Based Deep Fusion Neural Network Architecture for the Semantic Segmentation of Hyperspectral and Lidar Data,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Norton, C.L.[Cynthia L.], Hartfield, K.[Kyle], Collins, C.D.H.[Chandra D. Holifield], van Leeuwen, W.J.D.[Willem J. D.], Metz, L.J.[Loretta J.],
Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhou, L.[Lin], Geng, J.[Jie], Jiang, W.[Wen],
Joint Classification of Hyperspectral and LiDAR Data Based on Position-Channel Cooperative Attention Network,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, M.[Maqun], Gao, F.[Feng], Zhang, T.[Tiange], Gan, Y.H.[Yan-Hai], Dong, J.Y.[Jun-Yu], Yu, H.[Hui],
Attention Fusion of Transformer-Based and Scale-Based Method for Hyperspectral and LiDAR Joint Classification,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Wu, H.B.[Hai-Bin], Dai, S.Y.[Shi-Yu], Liu, C.Y.[Cheng-Yang], Wang, A.[Aili], Iwahori, Y.[Yuji],
A Novel Dual-Encoder Model for Hyperspectral and LiDAR Joint Classification via Contrastive Learning,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Zhang, M.M.[Meng-Meng], Li, W.[Wei], Zhang, Y.X.[Yu-Xiang], Tao, R.[Ran], Du, Q.[Qian],
Hyperspectral and LiDAR Data Classification Based on Structural Optimization Transmission,
Cyber(53), No. 5, May 2023, pp. 3153-3164.
IEEE DOI 2305
Laser radar, Feature extraction, Optimization, Indexes, Hyperspectral imaging, Collaboration, Task analysis, pattern recognition remote sensing BibRef

Song, H.[Huacui], Yang, Y.W.[Yuan-Wei], Gao, X.J.[Xian-Jun], Zhang, M.[Maqun], Li, S.H.[Shao-Hua], Liu, B.[Bo], Wang, Y.J.[Yan-Jun], Kou, Y.[Yuan],
Joint Classification of Hyperspectral and LiDAR Data Using Binary-Tree Transformer Network,
RS(15), No. 11, 2023, pp. 2706.
DOI Link 2306
BibRef

Hanuš, J.[Jan], Slezák, L.[Lukáš], Fabiánek, T.[Tomáš], Fajmon, L.[Lukáš], Hanousek, T.[Tomáš], Janoutová, R.[Ružena], Kopkáne, D.[Daniel], Novotný, J.[Jan], Pavelka, K.[Karel], Pikl, M.[Miroslav], Zemek, F.[František], Homolová, L.[Lucie],
Flying Laboratory of Imaging Systems: Fusion of Airborne Hyperspectral and Laser Scanning for Ecosystem Research,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Dong, W.Q.[Wen-Qian], Yang, T.[Teng], Qu, J.H.[Jia-Hui], Zhang, T.[Tian], Xiao, S.[Song], Li, Y.S.[Yun-Song],
Joint Contextual Representation Model-Informed Interpretable Network With Dictionary Aligning for Hyperspectral and LiDAR Classification,
CirSysVideo(33), No. 11, November 2023, pp. 6804-6818.
IEEE DOI 2311
BibRef

Huang, J.[Jing], Zhang, Y.H.[Ying-Hao], Yang, F.[Fang], Chai, L.[Li],
Attention-Guided Fusion and Classification for Hyperspectral and LiDAR Data,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Xu, H.T.[Hai-Tao], Zheng, T.[Tie], Liu, Y.Z.[Yu-Zhe], Zhang, Z.Y.[Zhi-Yuan], Xue, C.B.[Chang-Bin], Li, J.J.[Jiao-Jiao],
A Joint Convolutional Cross ViT Network for Hyperspectral and Light Detection and Ranging Fusion Classification,
RS(16), No. 3, 2024, pp. 489.
DOI Link 2402
BibRef

Wang, M.H.[Min-Hui], Sun, Y.X.[Ya-Xiu], Xiang, J.H.[Jian-Hong], Sun, R.[Rui], Zhong, Y.[Yu],
Joint Classification of Hyperspectral and LiDAR Data Based on Adaptive Gating Mechanism and Learnable Transformer,
RS(16), No. 6, 2024, pp. 1080.
DOI Link 2403
BibRef

Wang, H.Y.[Hao-Yu], Cheng, Y.[Yuhu], Liu, X.M.[Xiao-Min], Wang, X.S.[Xue-Song],
Reinforcement Learning Based Markov Edge Decoupled Fusion Network for Fusion Classification of Hyperspectral and LiDAR,
MultMed(26), 2024, pp. 7174-7187.
IEEE DOI 2405
Feature extraction, Laser radar, Task analysis, Topology, Data mining, Data integration, Remote sensing, graph learning BibRef

Li, Z.[Zirui], Liu, R.B.[Run-Bang], Sun, L.[Le], Zheng, Y.H.[Yu-Hui],
Multi-Feature Cross Attention-Induced Transformer Network for Hyperspectral and LiDAR Data Classification,
RS(16), No. 15, 2024, pp. 2775.
DOI Link 2408
BibRef

Wang, A.[Aili], Dai, S.Y.[Shi-Yu], Wu, H.B.[Hai-Bin], Iwahori, Y.[Yuji],
Multimodal Semantic Collaborative Classification for Hyperspectral Images and LiDAR Data,
RS(16), No. 16, 2024, pp. 3082.
DOI Link 2408
BibRef

Myagmarsuren, D.[Davaajargal], Wang, A.[Aili], Lv, H.R.[Hao-Ran], Wu, H.B.[Hai-Bin], Molnar, G.[Gabor], Yu, L.[Liang],
Joint Hyperspectral Images and LiDAR Data Classification Combined with Quantum-Inspired Entangled Mamba,
RS(17), No. 24, 2025, pp. 4065.
DOI Link 2512
BibRef

Zhang, J.Q.[Jia-Qing], Lei, J.[Jie], Xie, W.Y.[Wei-Ying], Yang, G.[Geng], Li, D.[Daixun], Li, Y.S.[Yun-Song],
Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification,
CirSysVideo(34), No. 8, August 2024, pp. 7643-7656.
IEEE DOI Code:
WWW Link. 2408
Feature extraction, Task analysis, Transformers, Mutual information, Laser radar, Redundancy, Data mining, self-distillation BibRef

Pan, H.Z.[Hai-Zhu], Li, X.[Xuan], Ge, H.[Haimiao], Wang, L.G.[Li-Guo], Shi, C.P.[Cui-Ping],
A Hierarchical Coarse-Fine Adaptive Fusion Network for the Joint Classification of Hyperspectral and LiDAR Data,
RS(16), No. 21, 2024, pp. 4029.
DOI Link 2411
BibRef

Wang, R.[Rui], Ye, X.X.[Xiao-Xi], Huang, Y.[Yao], Ju, M.[Ming], Xiang, W.[Wei],
GASSF-Net: Geometric Algebra Based Spectral-Spatial Hierarchical Fusion Network for Hyperspectral and LiDAR Image Classification,
RS(16), No. 20, 2024, pp. 3825.
DOI Link 2411
BibRef

Chen, T.[Tao], Chen, S.[Sizuo], Chen, L.[Luying], Chen, H.[Huayue], Zheng, B.[Bochuan], Deng, W.[Wu],
Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method,
RS(16), No. 22, 2024, pp. 4317.
DOI Link 2412
BibRef

Liu, J.[Jian], Xue, X.Z.[Xin-Zheng], Zuo, Q.[Qunyang], Ren, J.[Jie],
Classification of Hyperspectral-LiDAR Dual-View Data Using Hybrid Feature and Trusted Decision Fusion,
RS(16), No. 23, 2024, pp. 4381.
DOI Link 2501
BibRef

Wang, X.H.[Xiang-Hai], Song, L.Y.[Li-Yang], Feng, Y.N.[Yi-Ning], Zhu, J.H.[Jun-Heng],
S3F2Net: Spatial-Spectral-Structural Feature Fusion Network for Hyperspectral Image and LiDAR Data Classification,
CirSysVideo(35), No. 5, May 2025, pp. 4801-4815.
IEEE DOI Code:
WWW Link. 2505
Feature extraction, Laser radar, Land surface, Transformers, Data mining, Accuracy, Data integration, graph convolutional network (GCN) BibRef

Tian, Y.[Yu], Feng, Z.[Zehao], Tu, L.X.[Li-Xiao], Ji, C.N.[Chu-Ning], Han, J.Z.[Jia-Zheng], Zhao, Y.[Yibo], Zhou, Y.[You],
Exploring the Effectiveness of Fusing Synchronous/Asynchronous Airborne Hyperspectral and LiDAR Data for Plant Species Classification in Semi-Arid Mining Areas,
RS(17), No. 9, 2025, pp. 1530.
DOI Link 2505
BibRef

Liu, G.G.[Guan-Gen], Song, J.[Jiale], Chu, Y.H.[Yong-He], Zhang, L.C.[Lian-Chong], Li, P.[Peng], Xia, J.S.[Jun-Shi],
Deep Fuzzy Fusion Network for Joint Hyperspectral and LiDAR Data Classification,
RS(17), No. 17, 2025, pp. 2923.
DOI Link 2509
BibRef

Zhou, L.Y.[Liang-Yu], Luo, X.Y.[Xiao-Yan], Xue, R.[Rui],
Modal-aware contrastive learning for hyperspectral and LiDAR classification,
IVC(162), 2025, pp. 105669.
Elsevier DOI Code:
WWW Link. 2510
Contrastive learning, Attention mechanism, Image classification, Hyperspectral image (HSI), Light detection and ranging (LiDAR) BibRef

Hussain, K.M.[Khanzada Muzammil], Zhao, K.[Keyun], Zhou, Y.[Yang], Ali, A.[Aamir], Li, Y.[Ying],
Cross Attention Based Dual-Modality Collaboration for Hyperspectral Image and LiDAR Data Classification,
RS(17), No. 16, 2025, pp. 2836.
DOI Link 2509
BibRef

Hussain, K.M.[Khanzada Muzammil], Zhao, K.[Keyun], Pervaiz, S.[Sachal], Li, Y.[Ying],
Global-Local Mamba-Based Dual-Modality Fusion for Hyperspectral and LiDAR Data Classification,
RS(18), No. 1, 2026, pp. 138.
DOI Link 2601
BibRef

Liu, Z.Y.[Zheng-Yu], Yuan, X.[Xia], Yang, S.T.[Shu-Ting], Fu, G.Y.M.[Guan-Yi-Man], Zhao, C.X.[Chun-Xia], Xiong, F.C.[Feng-Chao],
Multimodal Prompt Tuning for Hyperspectral and LiDAR Classification,
RS(17), No. 16, 2025, pp. 2826.
DOI Link 2509
BibRef

Mei, Y.[Yong], Fan, J.L.[Jin-Long], Fan, X.[Xiangsuo], Li, Q.[Qi],
CSTC: Visual Transformer Network with Multimodal Dual Fusion for Hyperspectral and LiDAR Image Classification,
RS(17), No. 18, 2025, pp. 3158.
DOI Link 2510
BibRef

Wu, H.B.[Hai-Bin], Lv, H.R.[Hao-Ran], Wang, A.[Aili], Yan, S.Q.[Si-Qi], Molnar, G.[Gabor], Yu, L.[Liang], Wang, M.[Minhui],
CNN-GCN Coordinated Multimodal Frequency Network for Hyperspectral Image and LiDAR Classification,
RS(18), No. 2, 2026, pp. 216.
DOI Link 2602
BibRef

Shi, L.[Lulu], Li, C.C.[Chun-Chao], Zeng, Z.C.[Zheng-Chao], Duan, P.H.[Pu-Hong], Rasti, B.[Behnood], Plaza, A.[Antonio],
Masked Self-Attention Fusion Network for Joint Classification of Hyperspectral and LiDAR Data,
IP(35), 2026, pp. 346-360.
IEEE DOI Code:
WWW Link. 2602
Laser radar, Feature extraction, Transformers, Data mining, Computational modeling, Convolutional neural networks, self-attention mechanism BibRef

Wang, A.[Aili], Yao, M.[Manman], Lv, H.R.[Hao-Ran], Chen, H.S.[Hai-Song],
Text Semantic Guided Spatial-Frequency Fusion Network for HSI-LiDAR Land-Cover Classification,
RS(18), No. 12, 2026, pp. 1957.
DOI Link 2606
Hyperspectral-LiDAR. BibRef

Myagmarsuren, D.[Davaajargal], Wu, H.B.[Hai-Bin], Wang, A.[Aili],
Multimodal Uncertainty-Aware Gating Fusion and Iterative Feedback Refinement for HSI-LiDAR Open-Set Classification,
RS(18), No. 12, 2026, pp. 1963.
DOI Link 2606
BibRef

Shen, J.[Jie], Ma, Y.M.[Yi-Meng], Yang, H.[Houqun],
A Hierarchical Semantic Consistency Constraint Framework for Hyperspectral and LiDAR Data Joint Classification,
RS(18), No. 12, 2026, pp. 2058.
DOI Link 2606
BibRef

Zhou, S.[Shenbo], He, S.[Sibo], Li, D.[Daixun], Xie, W.Y.[Wei-Ying], Li, Y.S.[Yun-Song],
LMFusion: Breaking the Computational Barrier for Multimodal Classification in Remote Sensing,
RS(18), No. 12, 2026, pp. 1972.
DOI Link 2606
BibRef

Mohla, S., Pande, S., Banerjee, B., Chaudhuri, S.,
FusAtNet: Dual Attention based SpectroSpatial Multimodal Fusion Network for Hyperspectral and LiDAR Classification,
PBVS20(416-425)
IEEE DOI 2008
Feature extraction, Laser radar, Task analysis, Hyperspectral sensors, Sensors, Machine learning BibRef

Bigdeli, B., Samadzadegan, F., Reinartz, P.,
Classifier Fusion of Hyperspectral and Lidar Remote Sensing Data for Improvement of Land Cover Classifcation,
SMPR13(97-102).
DOI Link 1311
BibRef

Brook, A., Ben-Dor, E., Richter, R.,
Fusion of Hyperspectral Images and LIDAR data for Civil Engineering Structure Monitoring,
HighRes09(xx-yy).
PDF File. 0906
BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Image and Sensor Fusion for Cartography and Aerial Images, Satellite Images, Remote Sensing .