| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2023-02-23 |
| タイトル |
|
|
タイトル |
Use of a DNN-Based Image Translator with Edge Enhancement Technique to Estimate Correspondence between SAR and Optical Images |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
image registration |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
keypoint matching |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
synthetic aperture radar |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
deep neural network |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
generative adversarial networks |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 作成者 |
Toriya, Hisatoshi
Dewan, Ashraf
Ikeda, Hajime
Owada, Narihiro
Saadat, Mahdi
Inagaki, Fumiaki
Kawamura, Youhei
Kitahara, Itaru
|
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images. |
|
言語 |
en |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 書誌情報 |
en : APPLIED SCIENCES-BASEL
巻 12,
号 9,
発行日 2022
|
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2076-3417 |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 関連情報 |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/app12094159 |
| 権利情報 |
|
|
権利情報 |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
kokuA_2022_20.pdf (9.8 MB)
