Optics and Precision Engineering, Volume. 29, Issue 9, 2222(2021)
Remote sensing image feature extraction and classification based on contrastive learning method
[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] O A B PENATTI, K NOGUEIRA, SANTOS J ADOS. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? [C], 44-51(2015).
[4] D MARMANIS, M DATCU, T ESCH et al. Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geoscience and Remote Sensing Letters, 13, 105-109(2016).
[5] R ZHANG, P ISOLA, A A EFROS. Colorful image colorization, 649-666(2016).
[6] T CHEN, X H ZHAI, M RITTER et al. Self-supervised GANs via auxiliary rotation loss, 12146-12155(2019).
[8] K M HE, H Q FAN, Y X WU et al. Momentum contrast for unsupervised visual representation learning, 9726-9735(2020).
[13] M Caron, I Misra, J Mairal et al. Unsupervised Learning of Visual Features By Contrasting Cluster Assignments. Arxiv Preprint Arxiv, 09882, 2020(2006).
[14] J GRILL, F STRUB, F ALTCHE et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems, 33(2020).
[16] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(2016).
[18] D ULYANOV, A VEDALDI, V LEMPITSKY. Deep image prior. International Journal of Computer Vision, 128, 1867-1888(2020).
[20] G CHENG, J W HAN, X Q LU. Remote sensing image scene classification: benchmark and state of the art. Proceedings of the IEEE, 105, 1865-1883(2017).
[21] P HELBER, B BISCHKE, A DENGEL et al. EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 2217-2226(2019).
[22] Q ZOU, L H NI, T ZHANG et al. Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 12, 2321-2325(2015).
[23] Q Q ZHU, Y F ZHONG, B ZHAO et al. Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 13, 747-751(2016).
[24] S Kornblith, M Norouzi, H Lee et al. Similarity of Neural Network Representations Revisited, -3529-3519(2019).
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Xiao-dong MU, Kun BAI, Xuan-ang YOU, Yong-qing ZHU, Xue-bing CHEN. Remote sensing image feature extraction and classification based on contrastive learning method[J]. Optics and Precision Engineering, 2021, 29(9): 2222
Category: Information Sciences
Received: Jan. 29, 2021
Accepted: --
Published Online: Nov. 22, 2021
The Author Email: BAI Kun (nudt@foxmail. com)