| Title | Generative Adversarial Networks for Remote Sensing |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Feng, Jiayi |
| Conference Name | 2022 2nd International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR) |
| Date Published | nov |
| Keywords | Big Data, cloud removal, computer security, conditioanl generative adversarial networks, Deep Learning, Generative Adversarial Learning, generative adversarial networks, Metrics, pubcrawl, remote sensing, resilience, Resiliency, risk management, Scalability, semantic segmentation, Semantics |
| Abstract | Generative adversarial networks (GANs) have been increasingly popular among deep learning methods. With many GANs-based models developed since its emergence, among which are conditional generative adversarial networks, progressive growing of generative adversarial networks, Wasserstein generative adversarial networks and so on. These frameworks are currently widely applied in areas such as remote sensing cybersecurity, medical, and architecture. Especially, they have solved problems of cloud removal, semantic segmentation, image-to-image translation and data argumentation in remote sensing. For example, WGANs and ProGANs can be applied in data argumentation, and cGANs can be applied in semantic argumentation and image-to-image translation. This article provides an overview of structures of multiple GANs-based models and what areas they can be applied in remote sensing. |
| DOI | 10.1109/ICBAR58199.2022.00028 |
| Citation Key | feng_generative_2022 |
Generative Adversarial Networks for Remote Sensing