[1] Li D R, Tong Q X, Li R X et al. Some frontier scientific problems of high-resolution earth observation[J]. Scientia Sinica (Terrae), 42, 805-813(2012).
[2] Riaz F, Silva F B, Ribeiro M D et al. Invariant Gabor texture descriptors for classification of gastroenterology images[J]. IEEE Transactions on Biomedical Engineering, 59, 2893-2904(2012).
[3] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 60, 91-110(2004).
[4] Zhu Q Q, Zhong Y F, Zhao B et al. Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery[J]. IEEE Geoscience and Remote Sensing Letters, 13, 747-751(2016).
[5] Cheriyadat A M. Unsupervised feature learning for aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 439-451(2014).
[6] Othman E, Bazi Y, Alajlan N et al. Using convolutional features and a sparse autoencoder for land-use scene classification[J]. International Journal of Remote Sensing, 37, 2149-2167(2016).
[7] He Q, Li Y, Song W et al. Multimodal remote sensing image classification with small sample size based on high-level feature fusion[J]. Laser & Optoelectronics Progress, 56, 111001(2019).
[8] Qiu X H, Li M, Zhang L Q et al. Dual-band scene classification based on convolutional features and Bayesian decision[J]. Laser & Optoelectronics Progress, 58, 0415006(2021).
[9] Rostami M, Kolouri S, Eaton E et al. Deep transfer learning for few-shot SAR image classification[J]. Remote Sensing, 11, 1374(2019).
[10] Scott G J, England M R, Starms W A et al. Training deep convolutional neural networks for land-cover classification of high-resolution imagery[J]. IEEE Geoscience and Remote Sensing Letters, 14, 549-553(2017).
[11] Lima E, Sun X, Dong J Y et al. Learning and transferring convolutional neural network knowledge to ocean front recognition[J]. IEEE Geoscience and Remote Sensing Letters, 14, 354-358(2017).
[12] Zhai M, Liu H P, Sun F C. Lifelong learning for scene recognition in remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 16, 1472-1476(2019).
[13] Li H F, Cui Z Q, Zhu Z Q et al. RS-MetaNet: deep metametric learning for few-shot remote sensing scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 6983-6994(2021).
[14] Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning[C], 4077-4087(2017).
[15] Vinyals O, Blundell C, Lillicrap T et al. Matching networks for one shot learning[C], 29, 3630-3638(2016).
[16] Zhu S X, Zhou Z J, Gu X J et al. Scene classification of remote sensing images based on RCF network[J]. Laser & Optoelectronics Progress, 58, 1401001(2021).
[17] Wang P, Liu R, Xin X J et al. Scene classification of optical remote sensing images based on residual networks[J]. Laser & Optoelectronics Progress, 58, 0210001(2021).
[18] Alfattni G, Peek N, Nenadic G. Attention-based bidirectional long short-term memory networks for extracting temporal relationships from clinical discharge summaries[J]. Journal of Biomedical Informatics, 123, 103915(2021).
[19] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).
[20] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C](2015).
[21] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks[C], 1126-1135(2017).