Successful remote sensing image registration is one of the foundations of many remote sensing applications. Image high-lever features extracted by convolutional neural network (CNN) have achieved excellent performance in image classification and retrieval, and can be used to solve some problems of low-lever image registration features, such as the limitation of expression capability and easily being interfered. Hence, in this paper, the authors investigated the problem as to how to use CNN feature for remote sensing image registration. First, the authors investigated different CNN features from fully connected layers and aggregating convolutional features with different sizes from convolutional layer to register remote sensing image. Then the authors introduced the procedure by using CNN feature for image registration. Finally, the authors compared the registration performances of CNN features and scale-invariant feature transform (SIFT) features after the transformation of the image’s perspective, brightness and scale, respectively. The experimental results show that the CNN feature has better matching performance than the SIFT method in terms of matching accuracy and correct number of corresponding points. The finely tuned CNN feature has stronger robustness to the transformed image than the SIFT feature.
Gong M, Zhao S, Jiao L , et al. A novel coarse-to-fine scheme for automatic image registration based on sift and mutual information[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(7):4328-4338.
doi: 10.1109/TGRS.2013.2281391
Zhang Q, Jia Y H, Hu Z W . An improved SIFT algorithm for multi-source remote sensing image registration[J]. Geomatics and Information Science of Wuhan University, 2013,38(4):455-459.
Li S Y, Wang X T, Yang K . An improved SURF algorithm for color remote sensing image registration[J]. Computer Measurement and Control, 2017,25(1):209-212.
[5]
Yang K, Karlstrom L, Smith L C , et al. Automated high-resolution satellite image registration using supraglacial rivers on the Greenland ice sheet[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017,99:1-12.
[6]
Krizhevsky A, Sutskever I, Hinton G E . ImageNet classification with deep convolutional neural networks[J]. Advance in Neural Information Processing Systems, 2012,25(2):1097-1105.
[7]
Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition[EB/OL].( 2015- 04- 10). http://arxiv.org/pdf/1409.1556.pdf.
[8]
Chandrasekhar V, Lin J, Morère O , et al. A practical guide to CNNs and fisher vectors for image instance retrieval[J]. Signal Processing, 2015,128:426-439.
Zhang H Q, Liu X Y, Yang S , et al. Retrieval of remote sensing images based on semisupervised deep learning[J]. Journal of Remote Sensing, 2017,21(3):406-414.
Liu F, Shen T S, Ma X X . Convolutional neural network based multi-band ship target recognition with feature fusion[J]. Acta Optica Sinica, 2017,37(10):248-256.
[12]
Zhu G, Wang Q, Yuan Y , et al. SIFT on manifold:An intrinsic description[J]. Neurocomputing, 2013,113(7):227-233.
doi: 10.1016/j.neucom.2013.01.020
[13]
Yosinski J, Clune J, Bengio Y , et al. How transferable are features in deep neural networks?[C]//International Conference on Neural Information Processing Systems. MIT Press, 2014: 3320-3328.
[14]
Babenko A,Lempitsky V.Aggregating deep convolutional features for image retrieval[EB/OL].( 2015- 10- 26). http://arxiv.org/pdf/1510.07493v1.pdf.
[15]
Wei X S, Luo J H, Wu J , et al. Selective convolutional descriptor aggregation for fine-grained image retrieval[J]. IEEE Transactions on Image Processing, 2017,26(6):2868-2881.
doi: 10.1109/TIP.2017.2688133
[16]
Goncalves H, Goncalves J A, Corte-Real L . Measures for an objective evaluation of the geometric correction process quality[J]. IEEE Geoscience and Remote Sensing Letters, 2009,6(2):292-296.
doi: 10.1109/LGRS.2008.2012441
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