[1] Chen C, Yan J N, Wang L Z et al. Classification of urban functional areas from remote sensing images and time-series user behavior data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 1207-1221(2020).

[2] Zhang B, Sun X, Gao L R et al. Endmember extraction of hyperspectral remote sensing images based on the ant colony optimization (ACO) algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 49, 2635-2646(2011).

[3] Su H J, Yao W J, Wu Z Y et al. Kernel low-rank representation with elastic net for China coastal wetland land cover classification using GF-5 hyperspectral imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 238-252(2021).

[4] Liu J Y, Fan X, Jiang J et al. Learning a deep multi-scale feature ensemble and an edge-attention guidance for image fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 32, 105-119(2022).

[5] Wei W, Zhang J Y, Zhang L et al. Deep cube-pair network for hyperspectral imagery classification[J]. Remote Sensing, 10, 783(2018).

[6] Uezato T, Fauvel M, Dobigeon N. Hyperspectral image unmixing with LiDAR data-aided spatial regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 4098-4108(2018).

[7] Liao W Z, Pižurica A, Bellens R et al. Generalized graph-based fusion of hyperspectral and LiDAR data using morphological features[J]. IEEE Geoscience and Remote Sensing Letters, 12, 552-556(2015).

[8] Ghamisi P, Rasti B, Benediktsson J A. Multisensor composite kernels based on extreme learning machines[J]. IEEE Geoscience and Remote Sensing Letters, 16, 196-200(2019).

[9] Cao Q, Ma A L, Zhong Y F et al. Urban classification by multi-feature fusion of hyperspectral image and LiDAR data[J]. Journal of Remote Sensing, 23, 892-903(2019).

[10] Wang L G, Geng R Z, Dai Q L et al. Conditional random field classification method based on hyperspectral LiDAR fusion[J]. Infrared and Laser Engineering, 50, 562-573(2021).

[11] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C], 1097-1105(2012).

[12] Xu X D, Li W, Ran Q et al. Multisource remote sensing data classification based on convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 937-949(2018).

[13] Chen Y S, Li C Y, Ghamisi P et al. Deep fusion of remote sensing data for accurate classification[J]. IEEE Geoscience and Remote Sensing Letters, 14, 1253-1257(2017).

[14] Mohla S, Pande S, Banerjee B et al. FusAtNet: dual attention based SpectroSpatial multimodal fusion network for hyperspectral and LiDAR classification[C], 416-425(2020).

[15] Hang R L, Li Z, Ghamisi P et al. Classification of hyperspectral and LiDAR data using coupled CNNs[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 4939-4950(2020).

[16] Wu X, Hong D F, Chanussot J. Convolutional neural networks for multimodal remote sensing data classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5517010(2022).

[17] Hong D F, Gao L R, Hang R L et al. Deep encoder–decoder networks for classification of hyperspectral and LiDAR data[J]. IEEE Geoscience and Remote Sensing Letters, 19, 5500205(2022).

[18] Wang X H, Feng Y N, Song R X et al. Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data[J]. Information Fusion, 82, 1-18(2022).

[19] Wang J P, Li J, Shi Y L et al. AM³Net: adaptive mutual-learning-based multimodal data fusion network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 32, 5411-5426(2022).

[20] Roy S K, Deria A, Hong D F et al. Hyperspectral and LiDAR data classification using joint CNNs and morphological feature learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5530416(2022).

[21] Dong W Q, Zhang T, Qu J H et al. Multibranch feature fusion network with self- and cross-guided attention for hyperspectral and LiDAR classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5530612(2022).

[22] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth[EB/OL], 16-16. https://arxiv.org/abs/2010.11929v2

[23] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https://arxiv.org/abs/1706.03762

[24] Jin C, Tong C Q. Remote sensing image classification method based on fusion of CNN and transformer structure[J]. Laser & Optoelectronics Progress, 60, 2028006(2023).

[25] Chen Y H, Wang B, Yan Q Y et al. Hyperspectral remote sensing classification combining Transformer and multiscale residual mechanisms[J]. Laser & Optoelectronics Progress, 60, 1228002(2023).

[26] Zhao G R, Ye Q L, Sun L et al. Joint classification of hyperspectral and LiDAR data using a hierarchical CNN and transformer[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 5500716(2023).

[27] Chen T, Kornblith S, Norouzi M et al. A simple framework for contrastive learning of visual representations[C], 1597-1607(2020).

[28] Benediktsson J A, Palmason J A, Sveinsson J R. Classification of hyperspectral data from urban areas based on extended morphological profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 480-491(2005).

[29] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[30] 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).

[31] Rasti B, Ghamisi P, Gloaguen R. Hyperspectral and LiDAR fusion using extinction profiles and total variation component analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 3997-4007(2017).