1 |
Goodfellow, I.,J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, 2014. Generative adversarial nets, Advances in Neural Information Processing Systems, 27.
|
2 |
Zhang, Q., Q. Yuan, C. Zeng, X. Li, and Y. Wei, 2018. Missing data reconstruction in remote sensing image with a unified spatial-temporal-spectral deep convolutional neural network, IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4274-4288.
DOI
|
3 |
Chen, Y., K. Sun, D. Li, T. Bai, and W. Li, 2018. Improved relative radiometric normalization method of remote sensing images for change detection, Journal of Applied Remote Sensing, 12(4): 045018.
|
4 |
Ghamisi, P. and N. Yokoya, 2018. Img2dsm: Height simulation fromsingle imagery using conditional generative adversarial net, IEEE Geoscience and Remote Sensing Letters, 15(5): 794-798.
DOI
|
5 |
Helmer, E. H. and B. Ruefenacht, 2005. Cloud-free satellite image mosaics with regression trees and histogram matching, Photogrammetric Engineering & Remote Sensing, 71(9): 1079-1089.
DOI
|
6 |
LeCun, Y., B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and L.D. Jackel, 1989. Backpropagation applied to handwritten zip code recognition, Neural Computation, 1(4): 541-551.
DOI
|
7 |
Lee, S.B., W.Y. Park, Y.D. Eo, M.W. Pyeon, S. Han, S.H. Yeon, and B.K. Lee, 2017.Analysis on the applicability of simulated image from SPOT 4 HRVIR image, KSCE Journal of Civil Engineering, 21(4): 1434-1442.
DOI
|
8 |
Su, N., Y. Zhang, S. Tian, Y. Yan, and X. Miao, 2016. Shadow detection and removal for occluded object information recovery in urban high-resolution panchromatic satellite images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6): 2568-2582.
DOI
|
9 |
Badrinarayanan, V., A. Kendall, and R. Cipolla, 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495.
DOI
|
10 |
Andrade, H.J. and B.J. Fernandes, 2020. Synthesis of Satellite-Like Urban Images From Historical Maps Using Conditional GAN, IEEE Geoscience and Remote Sensing Letters, 19:1-4.
DOI
|
11 |
Choi, H.W., S.H. Lee, H.H. Kim, and Y.C. Suh, 2020. A Study on the Complementary Method of Aerial Image Learning Dataset Using Cycle Generative Adversarial Network, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 38(6): 499-509 (in Korean with English abstract).
DOI
|
12 |
Du, Y., P.M. Teillet, and J. Chihlar, 2002. Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection, Remote Sensing of Environment, 82(1): 123-134.
DOI
|
13 |
Guo, Q., M. He, and A. Li, 2018. High-resolution remote-sensing image registration based on angle matching of edge point features, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8): 2881-2895.
DOI
|
14 |
Isola, P., J.Y. Zhu, T. Zhou, and A. A. Efros, 2017. Image-to-image translation with conditional adversarial networks, Proc. of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii,Jul. 22-25, pp. 1125-1134.
|
15 |
Zhou, H., S. Liu, J. He, Q. Wen, L. Song, and Y. Ma, 2016. A new model for the automatic relative radiometric normalization of multiple images with pseudo-invariant features, International Journal of Remote Sensing, 37(19): 4554-4573.
DOI
|
16 |
Kim, H.J., D.K. Seo, Y.D. Eo, M.C. Jeon, and W.Y. Park, 2019.Multi-temporal nonlinear regression method for landsat image simulation, KSCE Journal of Civil Engineering, 23(2): 777-787.
DOI
|
17 |
Seo, D.K. and Y.D. Eo, 2019. Local-Based Iterative Histogram Matching for Relative Radiometric Normalization, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 37(5): 323-330.
DOI
|
18 |
Lee, J.M. and K.H. Bae, 2021.ApplicationTechnology of Image Inpainting Algorithm for Occlusion on Texture Image, Journal of Korean Society for GeospatialInformation Science, 29(4): 147-156 (in Korean with English abstract).
DOI
|
19 |
Lee, M.H., S.B. Lee, Y.D. Eo, S.W. Kim, J.H. Woo, and S.H. Han, 2017. A comparative study on generating simulated Landsat NDVI images using data fusion and regression method-the case of the Korean Peninsula, Environmental Monitoring and Assessment, 189(7): 1-13.
DOI
|
20 |
Seo, D.K. and Y.D. Eo, 2018. Relative radiometric normalization for high-spatial resolution satellite imagery based on multilayer perceptron, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 36(6): 515-523 (in Korean with English abstract).
DOI
|
21 |
Yoo, E.J. and D.C. Lee, 2010. Patch-based processing and occlusion area recovery for true orthoimage generation, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 28(1): 83-92 (in Korean with English abstract).
|
22 |
Yu,J., Z. Lin,J.Yang, X. Shen, X. Lu, andT. S. Huang, 2019. Free-form image inpainting with gated convolution, Proc. of the 2019 IEEE/CVF International Conference on Computer Vision, Long Beach, CA, Jun. 15-20, pp. 4471-4480.
|
23 |
Yuan, X., J. Tian, and P. Reinartz, 2020. Generating artificial near infrared spectral band from rgb image using conditional generative adversarial network, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3: 279-285.
|
24 |
Zhu, J.Y., T. Park, P. Isola, and A.A. Efros, 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks, Proc. of the 2017 IEEE International Conference on Computer Vision, Venice, Italy, Oct. 22-29, pp. 2223-2232.
|