期刊論文
| 學年 | 112 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2024-07-24 |
| 作品名稱 | Generative Adversarial Network Applied to Electromagnetic Imaging of Buried Objects |
| 作品名稱（其他語言） | |
| 著者 | Chien-Ching Chiu; Wei Chien; Ching-Lieh Li ;Po-Hsiang Chen; Kai-Xu Yu ;Eng-Hock Lim |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Sensors and Materials 36(7)， p.2925-2941 |
| 摘要 | Generative adversarial network (GAN) architecture is employed to tackle the inverse scattering problem of buried dielectric objects in half-space. Traditional iterative methods aimed at resolving the inverse scattering problem of buried dielectric objects have encountered a variety of difficulties, such as highly nonlinear phenomenon, high computational costs for half-space Green’s function, and missing measured scattered field information at the lower half of the object. The generator of GAN learns to generate more realistic images, while the discriminator of GAN improves its ability to identify fake images through a game-like process. The iterative process stops when the image generated by the generator is indistinguishable from the real image. In addition, we also analyze and compare the reconstruction outcomes obtained using both GAN and U-Net. Numerical outcomes show that GAN can efficiently reconstruct images with higher reliability than U-Net for buried objects with different dielectric permittivities and handwritten shapes. In summary, our proposed method has opened up a new avenue for imaging buried objects by adopting a deep learning network technique. |
| 關鍵字 | buried dielectric object; electromagnetic imaging; inverse scattering problems; generative adversarial network |
| 語言 | en_US |
| ISSN | 2435-0869 |
| 期刊性質 | 國內 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | Chien-Ching Chiu |
| 審稿制度 | 是 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126943 ) |