[1] Bhosle K, Musande V. Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images[J]. Journal of the Indian Society of Remote Sensing, 47, 1949-1958(2019).
[2] Zhao C H, Wang Y L, Qi B et al. Global and local real-time anomaly detectors for hyperspectral remote sensing imagery[J]. Remote Sensing, 7, 3966-3985(2015).
[3] Pipitone C, Maltese A, Dardanelli G et al. Monitoring water surface and level of a reservoir using different remote sensing approaches and comparison with dam displacements evaluated via GNSS[J]. Remote Sensing, 10, 71(2018).
[4] Marinelli D, Bovolo F, Bruzzone L. A novel change detection method for multitemporal hyperspectral images based on binary hyperspectral change vectors[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 4913-4928(2019).
[5] Pal M. Ensemble of support vector machines for land cover classification[J]. International Journal of Remote Sensing, 29, 3043-3049(2008).
[6] Chen Y, Nasrabadi N M, Tran T D. Classification for hyperspectral imagery based on sparse representation[C](2010).
[7] Li J, Bioucas-Dias J M, Plaza A. Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 48, 4085-4098(2010).
[8] Li J, Bioucas-Dias J M, Plaza A. Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields[J]. IEEE Transactions on Geoscience and Remote Sensing, 50, 809-823(2012).
[9] Chen Y S, Lin Z H, 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).
[10] Makantasis K, Karantzalos K, Doulamis A et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C], 4959-4962(2015).
[11] Chen Y S, Zhao X, Jia X P. Spectral-spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2381-2392(2015).
[12] Ma X R, Wang H Y, Geng J. Spectral-spatial classification of hyperspectral image based on deep auto-encoder[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 4073-4085(2016).
[13] Zhao W Z, Du S H. Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 4544-4554(2016).
[14] Zhang M M, Li W, Du Q. Diverse region-based CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 27, 2623-2634(2018).
[15] Lee H, Kwon H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 26, 4843-4855(2017).
[16] Liu J X, Ban W, Chen Y et al. Multi-dimensional CNN fused algorithm for hyperspectral remote sensing image classification[J]. Chinese Journal of Lasers, 48, 1610003(2021).
[17] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[18] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).
[19] Zhao C H, Li T, Feng S. Hyperspectral image classification based on dense convolution and domain adaptation[J]. Acta Photonica Sinica, 50, 0310001(2021).
[20] Zhong Z L, Li J, Luo Z M et al. Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 847-858(2018).
[21] Wang W J, Dou S G, Jiang Z M et al. A fast dense spectral-spatial convolution network framework for hyperspectral images classification[J]. Remote Sensing, 10, 1068(2018).
[22] Ma W P, Yang Q F, Wu Y et al. Double-branch multi-attention mechanism network for hyperspectral image classification[J]. Remote Sensing, 11, 1307(2019).
[23] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).
[24] Li R, Zheng S Y, Duan C X et al. Classification of hyperspectral image based on double-branch dual-attention mechanism network[J]. Remote Sensing, 12, 582(2020).
[25] Fu J, Liu J, Tian H J et al. Dual attention network for scene segmentation[C], 3141-3149(2019).
[26] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).
[29] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C], 1097-1105(2012).
