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

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

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

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

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

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

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

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

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

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

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

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

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

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

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