| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-09-11 |
| タイトル |
|
|
タイトル |
STLNet: Symmetric Transformer Learning Network for Remote Sensing Image Change Detection |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Change detection (CD) |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
local gather decoder (LGD) |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
remote sensing (RS) |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
rich semantic information |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
symmetric transformer |
| 資源タイプ |
|
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
| 著者 |
Mei, Liye
Huang, Andong
Ye, Zhaoyi
Yalikun, Yaxiaer
Wang, Ying
Xu, Chuan
Yang, Wei
Li, Xinghua
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Change detection (CD) signifies a pivotal domain within remote sensing image processing. The transformer has been introduced in the field of CD for its global perception capabilities. However, existing transformer-based methodologies serve primarily as mere direct feature extractors, rendering the attention mechanism within the decoder underutilized. To address this, we propose a symmetric transformer learning network (STLNet) specifically tailored for remote sensing image CD tasks, constructed entirely using transformers. The STLNet is designed to leverage the intrinsic capability of transformers to model extensive long-range dependencies effectively. This approach significantly bolsters the extraction of distinctive global-level features, thereby facilitating the accurate delineation of CD regions. Initially, we utilize an adaptive multigrain encoder to extract feature information from bitemporal images, thereby honing the focus on changing targets and providing deeper and more comprehensive information. Subsequently, we adopt an effective decoder architecture comprised of transformer structures, namely, local gather decoder (LGD). The LGD employs a multilevel semantic feature integration from the encoder to augment feature representation and interdependencies, crucial for detailing small changed areas effectively via a hierarchical attentional fusion block. Ultimately, the detection of changes is based on the rich semantic information provided by the LGD, enabling us to achieve enhanced precision in our remote sensing CD efforts. Results show that the proposed STLNet achieved F1 scores of 92.32% on the LEVIR-CD dataset, 90.01% on the WHU-CD dataset, and 82.15% on the SYSU-CD dataset, surpassing mainstream CD methods. |
| 書誌情報 |
en : IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
巻 18,
p. 2655-2667,
ページ数 13,
発行日 2024-12-17
|
| 出版者 |
|
|
出版者 |
IEEE |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2151-1535 |
| 出版者版DOI |
|
|
関連タイプ |
isReplacedBy |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1109/JSTARS.2024.3519305 |
| 出版者版URI |
|
|
関連タイプ |
isReplacedBy |
|
|
識別子タイプ |
URI |
|
|
関連識別子 |
https://ieeexplore.ieee.org/document/10804568 |
| 権利 |
|
|
権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
© 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| 著者版フラグ |
|
|
出版タイプ |
NA |
| 助成情報 |
|
|
|
助成機関名 |
Hubei Province |
|
|
研究課題番号 |
202319 |
| 助成情報 |
|
|
|
助成機関名 |
National Engineering Research Center of Geographic Information System |
| 助成情報 |
|
|
|
助成機関名 |
China University of Geosciences |
|
|
研究課題番号 |
2023KFJJ08 |
| 助成情報 |
|
|
|
助成機関名 |
Education Department of Hubei Province |
|
|
研究課題番号 |
B0233622 |
| 助成情報 |
|
|
|
助成機関名 |
Higher Education Institutions of Hubei Province |
|
|
研究課題番号 |
T2023045 |
| 助成情報 |
|
|
|
助成機関名 |
Hubei University of Technology |
|
|
研究課題番号 |
XJ2023007301 |
