Evaluating generative adversarial networks based image-level domain transfer for multi-source remote sensing image segmentation and object detection

References

• Arjovsky, M., S. Chintala, and L. Bottou. 2017. “Wasserstein GAN.” arXiv Preprint arXiv:1701.07875.
   Google Scholar
• Benjdira, B., Y. Bazi, A. Koubaa, and K. Ouni. 2019. “Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images.” Remote Sensing 11 (11): 1369. doi:10.3390/rs11111369.
   Web of Science ®Google Scholar
• Brock, A., J. Donahue, and K. Simonyan. 2019. “Large Scale GAN Training for High Fidelity Natural Image Synthesis.” Paper Presented at the International Conference on Learning Representations, New Orleans, April 30.
   Google Scholar
• Chen, C., W. Gong, Y. Chen, and W. Li. 2019. “Learning a Two-stage CNN Model for Multi-sized Building Detection in Remote Sensing Images.” Remote Sensing Letters 10 (2): 103–110. doi:10.1080/2150704X.2018.1528398.
   Web of Science ®Google Scholar
• Cubuk, E. D., B. Zoph, D. Mane, V. K. Vasudevan, and Q. V. Le. 2019. “AutoAugment: Learning Augmentation Strategies from Data.” Paper Presented at the Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Los Angeles, June 16-19.
   Google Scholar
• Gatys, L. A., A. S. Ecker, and M. Bethge. 2015. “A Neural Algorithm of Artistic Style.” arXiv Preprint arXiv:1508.06576.
   Google Scholar
• Goodfellow, I., J. Pougetabadie, M. Mirza, B. Xu, D. Wardefarley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative Adversarial Nets.” Paper Presented at the Annual Conference on Neural Information Processing Systems. Montreal. December 8-13.
   Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” Paper Presented at the Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas. June 26 - July 1.
   Google Scholar
• Hoffman, J., D. Wang, F. Yu, and T. Darrell. 2016. “FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation.” Paper Presented at the Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas. June 26 - July 1.
   Google Scholar
• Hoffman, J., E. Tzeng, T. Park, J. Zhu, P. Isola, K. Saenko, A. A. Efros, and T. Darrell. 2018. “CyCADA: Cycle-Consistent Adversarial Domain Adaptation.” Paper Presented at International Conference on Machine Learning, Stockholm, July 10-15.
   Google Scholar
• Inoue, H. 2018. “Data Augmentation by Pairing Samples for Images Classification.” arXiv Preprint arXiv:1801.02929.
   Google Scholar
• Isola, P., J. Zhu, T. Zhou, and A. A. Efros. 2017. “Image-to-Image Translation with Conditional Adversarial Networks.” Paper Presented at the Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Hawaii. July 21-26.
   Google Scholar
• Ji, S., S. Wei, and M. Lu. 2019. “Fully Convolutional Networks for Multisource Building Extraction from an Open Aerial and Satellite Imagery Data Set.” IEEE Transactions on Geoscience and Remote Sensing 57 (1): 574–586. doi:10.1109/TGRS.2018.2858817.
   Web of Science ®Google Scholar
• Kingma, D. P., and J. Ba. 2015. “Adam: A Method for Stochastic Optimization.” Paper Presented at International Conference on Learning Representations. San Diego. May 7-9.
   Google Scholar
• Kullback, S., and R. A. Leibler. 1951. “On Information and Sufficiency.” Annals of Mathematical Statistics 22 (1): 79–86. doi:10.1214/aoms/1177729694.
   Google Scholar
• Kussul, N., M. Lavreniuk, S. Skakun, and A. Shelestov. 2017. “Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data.” IEEE Geoscience and Remote Sensing Letters 14 (5): 778–782. doi:10.1109/LGRS.2017.2681128.
   Web of Science ®Google Scholar
• Laben, C. A., and B. V. Brower 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6011875, filed April 29.
   Google Scholar
• Li, D., M. Wang, and J. Pan. 2006. “Auto-dodging Processing and Its Application for Optical RS Images.” Geomatics and Information Science of Wuhan University 31 (9): 753–756.
   Google Scholar
• Li, Y., M. Liu, X. Li, M. Yang, and J. Kautz. 2018. “A Closed-Form Solution to Photorealistic Image Stylization.” Paper Presented at European Conference on Computer Vision. Munich. September 8-14.
   Google Scholar
• Long, M., Y. Cao, J. Wang, and M. I. Jordan. 2015. “Learning Transferable Features with Deep Adaptation Networks.” Paper Presented at International Conference on Machine Learning. Lille. July 6-11.
   Google Scholar
• Luan, F., S. Paris, E. Shechtman, and K. Bala. 2017. “Deep Photo Style Transfer.” Paper Presented at the Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Hawaii. July 21-26.
   Google Scholar
• Maggiori, E., Y. Tarabalka, G. Charpiat, and P. Alliez. 2017. “Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark.” Paper Presented at International Geoscience and Remote Sensing Symposium. Fort Worth. July 23-28.
   Google Scholar
• Majtey, A. P., P. W. Lamberti, M. T. Martin, and A. Plastino. 2005. “Wootters’ Distance Revisited: A New Distinguishability Criterium.” European Physical Journal D 32 (3): 413–419. doi:10.1140/epjd/e2005-00005-1
   Web of Science ®Google Scholar
• Maskin, E. 1999. “Nash Equilibrium and Welfare Optimality.” The Review of Economic Studies 66 (1): 23–38. doi:10.1111/1467-937X.00076.
   Web of Science ®Google Scholar
• Mejjati, Y. A., C. Richardt, J. Tompkin, D. Cosker, and K. I. Kim. 2018. “Unsupervised Attention-guided Image-to-Image Translation.” Paper Presented at the Annual Conference on Neural Information Processing Systems. Montreal. December 3-8.
   Google Scholar
• Muthoo, A., M. J. Osborne, and A. Rubinstein. 1996. “A Course in Game Theory.” Economica 63 (249). doi:10.2307/2554642.
   Web of Science ®Google Scholar
• Olkin, I., and F. Pukelsheim. 1982. “The Distance between Two Random Vectors with Given Dispersion Matrices.” Linear Algebra and Its Applications 48: 257–263. doi:10.1016/0024-3795(82)90112-4.
   Web of Science ®Google Scholar
• Patel, V. M., R. Gopalan, R. Li, and R. Chellappa. 2015. “Visual Domain Adaptation: A Survey of Recent Advances.” IEEE Signal Processing Magazine 32 (3): 53–69. doi:10.1109/MSP.2014.2347059.
   Web of Science ®Google Scholar
• Perez, L., and J. Wang. 2017. “The Effectiveness of Data Augmentation in Image Classification Using Deep Learning.” Paper Presented at the Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Hawaii. July 21-26.
   Google Scholar
• Pitie, F., A. Kokaram, and R. Dahyot. 2007. “Automated Colour Grading Using Colour Distribution Transfer.” Computer Vision and Image Understanding 107 (1): 123–137. doi: 10.1016/j.cviu.2006.11.011.
   Web of Science ®Google Scholar
• Pitie, F., and A. Kokaram. 2007. “The Linear Monge-Kantorovitch Linear Colour Mapping for Example-based Colour Transfer.” Paper Presented at European Conference on Visual Media Production. London. November 27-28.
   Google Scholar
• Pitie, F., A. Kokaram, and R. Dahyot. 2005. “N-dimensional Probability Density Function Transfer and Its Application to Color Transfer.” Paper Presented at IEEE International Conference on Computer Vision. Beijing. October 17-20.
   Google Scholar
• Reinhard, E., M. Adhikhmin, B. Gooch, and P. Shirley. 2001. “Color Transfer between Images.” IEEE Computer Graphics and Applications 21 (5): 34–41. doi:10.1109/38.946629.
   Web of Science ®Google Scholar
• Ren, S., K. He, R. Girshick, and J. Sun. 2015. “Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks.” Paper Presented at the Annual Conference on Neural Information Processing Systems. Montreal. December 11-12.
   Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” Paper Presented at the International Conference on Medical Image Computing and Computer Assisted Intervention. Munich. October 5-9.
   Google Scholar
• Santos, A. A., J. M. Junior, M. S. Araujo, D. R. Martini, E. C. Tetila, H. L. Siqueira, C. Aoki et al. 2019. “Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs.” Sensors 19 (16): 3595. DOI:10.3390/s19163595.
   Web of Science ®Google Scholar
• Vermote, E., D. Tanre, J. L. Deuze, M. Herman, and J. J. Morcette. 1997. “Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview.” IEEE Transactions on Geoscience and Remote Sensing 35 (3): 675–686. doi:10.1109/36.581987.
   Web of Science ®Google Scholar
• Vrhel, M. J., and H. J. Trussell. 1992. “Color Correction Using Principal Components.” Color Research and Application 17 (5): 328–338. doi:10.1002/col.5080170507.
   Web of Science ®Google Scholar
• Wang, Y., S. Ji, M. Lu, and Y. Zhang. 2020. “Attention Boosted Bilinear Pooling for Remote Sensing Image Retrieval.” International Journal of Remote Sensing 41 (7): 2704–2724. doi:10.1080/01431161.2019.1697010.
   Web of Science ®Google Scholar
• Wang, Z., and A. C. Bovik. 2002. “A Universal Image Quality Index.” IEEE Signal Processing Letters 9 (3): 81–84. doi:10.1109/97.995823.
   Web of Science ®Google Scholar
• Yoo, J. D., M. K. Park, J. Cho, and K. H. Lee. 2013. “Local Color Transfer between Images Using Dominant Colors.” Journal of Electronic Imaging 22 (3): 033003. doi:10.1117/1.JEI.22.3.033003.
   Web of Science ®Google Scholar
• Zhang, C., I. Sargent, X. Pan, H. Li, A. Gardiner, J. S. Hare, and P. M. Atkinson. 2019. “Joint Deep Learning for Land Cover and Land Use Classification.” Remote Sensing of Environment 221: 173–187. doi:10.1016/j.rse.2018.11.014.
   Web of Science ®Google Scholar
• Zhang, H., M. Cissé, Y. Dauphin, and D. Lopez-Paz. 2017. “Mixup: Beyond Empirical Risk Minimization.” arXiv Preprint arXiv:1710.09412.
   Google Scholar
• Zhu, J., T. Park, P. Isola, and A. A. Efros. 2017. “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks.” Paper Presented at IEEE International Conference on Computer Vision. Venice. October 22-29.
   Google Scholar