[1] REN X, MALIK J. Learning a classification model for segmentation[C], 2, 10(2003).

[2] 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).

[3] CAMPS-VALLS G, GOMEZ-CHOVA L, MUÑOZ-MARÍ J et al. Composite kernels for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 3, 93-97(2006).

[4] TARABALKA Y, RANA A. Graph-cut-based model for spectral-spatial classification of hyperspectral images[C], 3418-3421(2014).

[5] CHEN P H, LIN C J, SCHÖLKOPF B. A tutorial on ν-support vector machines[J]. Applied Stochastic Models in Business and Industry, 21, 111-136(2005).

[6] CRAMER J S. The origins of logistic regression[M]. SSRN Electronic Journal(2002).

[7] BREIMAN L. Random forests[J]. Machine Learning, 45, 5-32(2001).

[8] CHEN Y, LIN Z, ZHAO X et al. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2094-2107(2014).

[9] LI T, ZHANG J, ZHANG Y. Classification of hyperspectral image based on deep belief networks[C], 5132-5136(2014).

[10] KALANTARI L, GADER P, GRAVES S et al. Evaluating similarity measures for hyperspectral classification of tree species at Ordway-Swisher Biological Station[C], 2691-2694(2014).

[11] LIU Q, ZHOU F, HANG R et al. Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification[J]. Remote Sensing, 9, 1330(2017).

[12] ARUN P V, BUDDHIRAJU K M, PORWAL A. Capsulenet-based spatial–spectral classifier for hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1849-1865(2019).

[13] PAN E, MEI X, WANG Q et al. Spectral-spatial classification for hyperspectral image based on a single GRU[J]. Neurocomputing, 387, 150-160(2020).

[14] HE X, CHEN Y, LIN Z. Spatial-spectral transformer for hyperspectral image classification[J]. Remote Sensing, 13, 498(2021).

[15] SHI Yuetian, FU Bin, WANG Nan et al. Spectral-spatial attention rotation-invariant classification network for airborne hyperspectral images[J]. Drones, 7, 1-30(2023).

[16] KAMNITSAS K, LEDIG C, NEWCOMBE V F J et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation[J]. Medical Image Analysis, 36, 61-78(2017).

[17] HE K, ZHANG X, REN S et al. Identity mappings in deep residual networks[C], 630-645(2016).

[18] ISSITT R W, CORTINA-BORJA M, BRYANt W et al. Classification performance of neural networks versus logistic regression models: evidence from healthcare practice[J]. Cureus, 14, e22443(2022).

[19] MA W, GONG C, HU Y et al. The Hughes phenomenon in hyperspectral classification based on the ground spectrum of grasslands in the region around Qinghai Lake[C], 8910, 363-373(2013).

[20] KHODR J, YOUNES R. Dimensionality reduction on hyperspectral images: a comparative review based on artificial datas[C], 4, 1875-1883(2011).

[21] AHMAD M, SHABBIR S, RAZA R A et al. Hyperspectral image classification: Artifacts of dimension reduction on hybrid CNN[J]. arXiv preprint(2021).

[22] DU Q, FOWLER J E. Hyperspectral image compression using JPEG2000 and principal component analysis[J]. IEEE Geoscience and Remote Sensing Letters, 4, 201-205(2007).

[23] LI W, CHEN C, ZHANG M et al. Data augmentation for hyperspectral image classification with deep CNN[J]. IEEE Geoscience and Remote Sensing Letters, 16, 593-597(2018).

[24] SANTURKAR S, TSIPRAS D, ILYAS A et al. How does batch normalization help optimization？[J]. Advances in Neural Information Processing Systems, 31(2018).

[25] SRIVASTAVA N, HINTON G, KRIZHEVSKY A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 15, 1929-1958(2014).

[26] ZHANG Z, SABUNCU M. Generalized cross entropy loss for training deep neural networks with noisy labels[J]. Advances in Neural Information Processing Systems, 31(2018).

[27] KINGMA D P, BA J. Adam: A method for stochastic optimization[J]. arXiv preprint(2014).

[28] SEXTON R S, DORSEY R E, JOHNSON J D. Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing[J]. European Journal of Operational Research, 114, 589-601(1999).

[29] ALDOUS D. On the Markov chain simulation method for uniform combinatorial distributions and simulated annealing[J]. Probability in the Engineering and Informational Sciences, 1, 33-46(1987).

[30] MERCIER G, LENNON M. Support vector machines for hyperspectral image classification with spectral-based kernels[C], 288-290(2003).

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

[32] LEE H, KWON H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 26, 4843-4855(2017).

[33] LI R, ZHENG S, DUAN C et al. Classification of hyperspectral image based on double-branch dual-attention mechanism network[J]. Remote Sensing, 12, 582(2020).

[34] MA W, YANG Q, WU Y et al. Double-branch multi-attention mechanism network for hyperspectral image classification[J]. Remote Sensing, 11, 1307(2019).