[1] [1] 1陈科峻， 张叶. 循环神经网络多标签航空图像分类［J］. 光学 精密工程， 2020， 28（6）： 1404-1413. doi: 10.3788/ope.20202806.1404CHENK J， ZHANGY. Recurrent neural network multi-label aerial images classification［J］. Opt. Precision Eng.， 2020， 28（6）： 1404-1413. （in Chinese）. doi: 10.3788/ope.20202806.1404

[2] [2] 2杨州， 慕晓冬， 王舒洋， 等. 基于多尺度特征融合的遥感图像场景分类［J］. 光学 精密工程， 2018， 26（12）： 3099-3107. doi: 10.3788/ope.20182612.3099YANGZH， MUX D， WANGSH Y， et al. Scene classification of remote sensing images based on multiscale features fusion［J］. Opt. Precision Eng.， 2018， 26（12）： 3099-3107. （in Chinese）. doi: 10.3788/ope.20182612.3099

[3] PENATTI O A B, NOGUEIRA K, ADOS SANTOS J. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains？ ［C］[C], 44-51(2015).

[4] MARMANIS D, DATCU M, ESCH T et al. Deep learning earth observation classification using ImageNet pretrained networks[J]. IEEE Geoscience and Remote Sensing Letters, 13, 105-109(2016).

[5] ZHANG R, ISOLA P, EFROS A A[M]. Colorful image colorization, 649-666(2016).

[6] CHEN T, ZHAI X H, RITTER M et al. Self-supervised GANs via auxiliary rotation loss[C], 12146-12155(2019).

[7] CHEN T, KORNBLITH S, NOROUZI M et al. A simple framework for contrastive learning of visual representations[webpage]. Arxiv Preprint Arxiv, 05709, 2020(2002). https：//arxiv.org/abs/2002.05709v1

[8] HE K M, FAN H Q, WU Y X et al. Momentum contrast for unsupervised visual representation learning[C], 9726-9735(2020).

[9] Chen X, Fan H, Girshick R et al. Improved baselines with momentum contrastive learning[webpage]. Arxiv Preprint Arxiv, 04297, 2020(2003). https：//www.arxiv-vanity.com/papers/2003. 04297/

[10] Kalantidis Y, Sariyildiz M B, Pion N et al. Hard negative mixing for contrastive learning[webpage]. Arxiv Preprint Arxiv, 01028, 2020(2010). https：//arxiv.org/abs/2010.01028

[11] LI T H, FAN L J, YUAN Y et al. Information-preserving contrastive learning for self-supervised representations[webpage]. Arxiv Preprint Arxiv, 09962, 2020(2012). https：//arxiv.org/abs/2012.09962v1

[12] LIU H, ABBEEL P. Hybrid discriminative-generative training via contrastive learning[webpage]. Arxiv Preprint Arxiv, 09070, 2020(2007). https：//arxiv.org/abs/2007.09070

[13] Caron M, Misra I, Mairal J et al. Unsupervised Learning of Visual Features By Contrasting Cluster Assignments[webpage]. Arxiv Preprint Arxiv, 09882, 2020(2006).

[14] GRILL J, STRUB F, ALTCHE F et al. Bootstrap your own latent-a new approach to self-supervised learning[J]. Advances in Neural Information Processing Systems, 33(2020).

[15] Chen X, He K. Exploring Simple Siamese Representation Learning[webpage]. Arxiv Preprint Arxiv, 10566(2011). https：//arxiv.org/abs/2011.10566

[16] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[17] CHEN W J, PU S L, XIE D et al. Unsupervised image classification for deep representation learning[webpage]. Arxiv Preprint Arxiv, 11480, 2020(2006). https：//arxiv.org/abs/2006.11480v1

[18] ULYANOV D, VEDALDI A, LEMPITSKY V. Deep image prior[J]. International Journal of Computer Vision, 128, 1867-1888(2020).

[19] Zhu J, Li Y, Zhou SK. Aggregative Self-supervised Feature Learning[webpage]. Arxiv Preprint Arxiv, 07477(2012). https：//arxiv.org/abs/2012.07477

[20] CHENG G, HAN J W, LU X Q. Remote sensing image scene classification： benchmark and state of the art[J]. Proceedings of the IEEE, 105, 1865-1883(2017).

[21] HELBER P, BISCHKE B, DENGEL A et al. EuroSAT： a novel dataset and deep learning benchmark for land use and land cover classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 2217-2226(2019).

[22] ZOU Q, NI L H, ZHANG T et al. Deep learning based feature selection for remote sensing scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 12, 2321-2325(2015).

[23] 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).

[24] Kornblith S, Norouzi M, Lee H et al. Similarity of Neural Network Representations Revisited[C], -3529-3519(2019).