[1] TIAN Di, YAN Zhengbing, FANG Jingyun. Review on characteristics and main hypotheses of plant ecological stoichiometry. Journal of Plant Ecology, 45, 682-713(2021).

[2] PAOLETTI M E, HAUT J M, PLAZA J et al. Deep learning classifiers for hyperspectral imaging： A review. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 279-317(2019).

[3] LU X, LIU G, NING S et al. Tree species classification based on airborne Lidar and hyperspectral data, 2787-2790(2020).

[4] HSIEH T H, KIANG J F. Comparison of CNN algorithms on hyperspectral image classification in agricultural lands. Sensors, 20, 1734(2020).

[5] BERA S, SHRIVASTAVA V K, SATAPATHY S C. Advances in hyperspectral image classification based on convolutional neural networks： A review. Cmes-Computer Modeling in Engineering & Sciences, 133, 219-250(2022).

[6] ZHAO Ling, ZHANG Xiaoling, WU Yyanshuang et al. 3D-CNN subtropical forest tree species classification for airborne hyperspectral data. Forestry Science, 56, 97-107(2020).

[7] LIANG Linanhui, LI Jun, ZHANG Shaoquan. Hyperspectral image classification method based on 3D Octave convolution and Bi-RNN attention network. Journal of Photonics, 50, 284-296(2021).

[8] ZHOU B, LI B, HE X et al. A spectral-spatial cooperative noise-evaluation method for hyperspectral imaging. Current Optics and Photonics, 4, 530-539(2020).

[9] LI L, YIN J, JIA X et al. Joint spatial-spectral attention network for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 18, 1816-1820(2020).

[10] MOU L, GHAMISI P, ZHU X X. Deep recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 3639-3655(2017).

[11] CHEN Y, FAN H, XU B et al. Drop an octave： Reducing spatial redundancy in convolutional neural networks with octave convolution, 3435-3444(2019).

[12] XU Q, XIAO Y, WANG D et al. CSA-MSO3DCNN： Multiscale octave 3D CNN with channel and spatial attention for hyperspectral image classification. Remote Sensing, 12, 188-212(2020).

[13] MAO Xuegang, CHEN Wenqu, WEI Jingyu et al. Impact and evaluation of partitioning scale on object-oriented tree species classification. Forestry Science, 53, 73-83(2017).

[14] YIN Y, WEI L. Hyperspectral image classification using comprehensive evaluation model of extreme learning machine based on cumulative variation weights. IEEE Access, 8, 187991-188003(2020).

[15] JIN E, DU J, BI Y et al. Research on classification of grassland degeneration indicator objects based on UAV hyperspectral remote sensing and 3D_RNet-O model. Sensors, 24, 1114(2024).

[16] FIRAT H, ASKER M E, HANBAY D. Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNN. Remote Sensing Applications：Society and Environment, 25, 100694(2022).