[1] Hu H Y, Hui Z Y, Li N et al. Airborne LiDAR point cloud classification based on multiple-entity eigenvector fusion[J]. Chinese Journal of Lasers, 47, 0810002(2020).

[2] Kong D M, Xu L J, Li X L et al. A real-time method for DSM generation from airborne LiDAR data[C]. //2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May 6-9, 2013, Minneapolis, MN, USA., 377-380(2013).

[3] Naidoo L, Cho M A, Mathieu R et al. Classification of savanna tree species, in the greater Kruger national park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 167-179(2012).

[4] Khodadadzadeh M, Li J, Prasad S et al. Fusion of hyperspectral and LiDAR remote sensing data using multiple feature learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2971-2983(2015). http://smartsearch.nstl.gov.cn/paper_detail.html?id=7e34555f4f73a120121e74f2a550115b

[5] Ghamisi P, Höfle B. LiDAR data classification using extinction profiles and a composite kernel support vector machine[J]. IEEE Geoscience and Remote Sensing Letters, 14, 659-663(2017). http://ieeexplore.ieee.org/document/7873288

[6] Ghamisi P, Höfle B, Zhu X X et al. Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 3011-3024(2017). http://ieeexplore.ieee.org/document/7786851/

[7] Xia J S, Yokoya N, Iwasaki A et al. Fusion of hyperspectral and LiDAR data with a novel ensemble classifier[J]. IEEE Geoscience and Remote Sensing Letters, 15, 957-961(2018). http://ieeexplore.ieee.org/document/8333752

[8] Wang A L, He X, Ghamisi P et al. LiDAR data classification using morphological profiles and convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 15, 774-778(2018). http://ieeexplore.ieee.org/document/8319917

[9] Wang A L, Wang M H, Jiang K Y et al. A novel LiDAR data classification algorithm combined densenet with STN[C]. //2019 IEEE International Geoscience and Remote Sensing Symposium, July 28-August 2, 2019, Yokohama, Japan., 2483-2486(2019).

[10] Ge C R, Du Q, Li W et al. Hyperspectral and LiDAR data classification using kernel collaborative representation based residual fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1963-1973(2019). http://ieeexplore.ieee.org/document/8718389/

[11] Wang P, Liu R, Xin X J et al. Scene classification of optical remote sensing images based on residual network[J]. Laser & Optoelectronics Progress, 58, 021001(2021).

[12] Zhang X D, Wang T J, Yang Y et al. Classification of small-sized sample hyperspectral images based on multi-scale residual network[J]. Laser & Optoelectronics Progress, 57, 162801(2020).

[13] Zhu T Y, Huang L F, Dong F et al. Infrared-remote-sensing ship detection based on lightweight residual network[J]. Acta Optica Sinica, 40, 0111018(2020).

[14] Sabour S, Frosst N, Hinton G E et al. Dynamic routing between capsules[C]. //Advances in Neural Information Processing Systems (NIPS), December 4-9, 2017, Long Beach, CA, USA, 3856-3866(2017).

[15] Raza A, Huo H, Sirajuddin S et al. Diverse capsules network combining multiconvolutional layers for remote sensing image scene classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5297-5313(2020). http://www.researchgate.net/publication/344057007_Diverse_Capsules_Network_Combining_Multiconvolutional_Layers_for_Remote_Sensing_Image_Scene_Classification

[16] Wang X, Tan K, Du Q et al. Caps-TripleGAN: GAN-assisted CapsNet for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 7232-7245(2019). http://ieeexplore.ieee.org/document/8710617/

[17] Chang S W, Liu J. Multi-lane capsule network for classifying images with complex background[J]. IEEE Access, 8, 79876-79886(2020). http://ieeexplore.ieee.org/document/9079546

[18] Paoletti M E, Haut J M, Beltran R F et al. Capsule networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 2145-2160(2019). http://ieeexplore.ieee.org/document/8509610

[19] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[20] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[C]. //International Conference on Learning Representations (ICLR), May 2-4, 2016, Caribe Hilton, San Juan, Puerto Rico, 1-13(2016).

[21] Yu F, Koltun V, Funkhouser T et al. Dilated residual networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 636-644(2017).

[22] Wang P Q, Chen P F, Yuan Y et al. Understanding convolution for semantic segmentation[C]. //2018 IEEE Winter Conference on Applications of Computer Vision (WACV), March 12-15, 2018, Lake Tahoe, NV, USA., 1451-1460(2018).

[23] Debes C, Merentitis A, Heremans R et al. Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2405-2418(2014). http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6776408&queryText%3DHyperspectral+and+LiDAR+Data+Fusion%3A+Outcome+of+the+2013+GRSS+Data+Fusion+Contest