[1] Bouguettaya A, Zarzour H, Kechida A et al. Deep learning techniques to classify agricultural crops through UAV imagery: a review[J]. Neural Computing & Applications, 34, 9511-9536(2022).

[2] Mao Y W, Guo Y, Zhang W F et al. Tree species classification by combining LiDAR, hyperspectral data and 3D-CNN method[J]. Scientia Silvae Sinicae, 59, 73-83(2023).

[3] Zheng Z S, Liu B, Lu P et al. Spectral classification and characteristic spectral analysis of nearshore aquatic plants based on AlexNet[J]. Chinese Journal of Lasers, 50, 0211001(2023).

[4] Wambugu N, Chen Y P, Xiao Z L et al. Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: a review[J]. International Journal of Applied Earth Observation and Geoinformation, 105, 102603(2021).

[5] Zhong Y F, Wang X Y, Hu X et al. Hyperspectral with high-spatial resolution remote sensing from observation, processing to applications[J]. Acta Geodaetica et Cartographica Sinica, 52, 1212-1226(2023).

[6] Rasti B, Hong D F, Hang R L et al. Feature extraction for hyperspectral imagery: the evolution from shallow to deep: overview and toolbox[J]. IEEE Geoscience and Remote Sensing Magazine, 8, 60-88(2020).

[7] Jia S, Jiang S G, Lin Z J et al. A survey: deep learning for hyperspectral image classification with few labeled samples[J]. Neurocomputing, 448, 179-204(2021).

[8] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778.

[9] Hu J, Chen N, Xu W C et al. Three-dimensional lane detection algorithm of lidar based on adaptive gating and dual pathways[J]. Chinese Journal of Lasers, 50, 2210001(2023).

[10] Song W W, Li S T, Fang L Y et al. Hyperspectral image classification with deep feature fusion network[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 3173-3184(2018).

[11] Cheng R J, Yang Y, Li L W et al. Lightweight residual network based on depthwise separable convolution for hyperspectral image classification[J]. Acta Optica Sinica, 43, 1228010(2023).

[12] Roy S K, Manna S, Song T C et al. Attention-based adaptive spectral-spatial kernel ResNet for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 7831-7843(2021).

[13] Zhang X D, Wang T J, Zhu S J et al. Hyperspectral image classification based on dilated convolutional attention neural network[J]. Acta Optica Sinica, 41, 0310001(2021).

[14] Wang X, Fan Y G. Hyperspectral image classification based on modified DenseNet and spatial spectrum attention mechanism[J]. Laser & Optoelectronics Progress, 59, 0210014(2022).

[15] Roy S K, Krishna G, Dubey S R et al. HybridSN: exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 17, 277-281(2020).

[16] Feng F, Wang S T, Zhang J et al. Hyperspectral images classification based on multi-feature fusion and hybrid convolutional neural networks[J]. Laser & Optoelectronics Progress, 58, 0810010(2021).

[17] Zhang J, Wei F Y, Feng F et al. Spatial-spectral feature refinement for hyperspectral image classification based on attention-dense 3D-2D-CNN[J]. Sensors, 20, 5191(2020).

[18] Feng F, Zhang Y S, Zhang J et al. Small sample hyperspectral image classification based on cascade fusion of mixed spatial-spectral features and second-order pooling[J]. Remote Sensing, 14, 505(2022).

[19] Xue Z H, Xu Q, Zhang M X. Local transformer with spatial partition restore for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 4307-4325(2022).

[20] Sun L, Zhao G R, Zheng Y H et al. Spectral-spatial feature tokenization transformer for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5522214(2022).

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

[22] Hou S K, Shi H Y, Cao X H et al. Hyperspectral imagery classification based on contrastive learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5521213(2022).

[23] Hang R L, Li C X, Liu Q S. Inter-spectral contrast learning based unsupervised feature extraction for hyperspectral images[J]. Acta Geodaetica et Cartographica Sinica, 52, 1164-1174(2023).

[24] Zhu M Z, Fan J Y, Yang Q H et al. SC-EADNet: a self-supervised contrastive efficient asymmetric dilated network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5519517(2022).

[25] Zhao L, Luo W Q, Liao Q M et al. Hyperspectral image classification with contrastive self-supervised learning under limited labeled samples[J]. IEEE Geoscience and Remote Sensing Letters, 19, 6008205(2022).

[26] Liu B, Yu A Z, Yu X C et al. Deep multiview learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 7758-7772(2021).

[27] Ibtehaz N, Rahman M S. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 121, 74-87(2020).

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

[29] Li N, Zhao H J, Jia G R. Dimensional reduction method based on factor analysis model for hyperspectral data[J]. Journal of Image and Graphics, 16, 2030-2035(2011).

[30] Zhong Y F, Hu X, Luo C et al. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF[J]. Remote Sensing of Environment, 250, 112012(2020).

[31] Chen X L, He K M. Exploring simple siamese representation learning[C], 15745-15753.

[32] He K M, Chen X L, Xie S N et al. Masked autoencoders are scalable vision learners[C], 15979-15988.