Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection
文献类型：期刊论文
| 作者 | Zhou, Yuan1; Feng, Yanjie1; Huo, Shuwei1; Li, Xiaofeng2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60页码:14 |
| 关键词 | Feature extraction Frequency-domain analysis Remote sensing Optical sensors Optical imaging Frequency domain analysis Optical fiber networks Change detection deep learning frequency domain neural network optical image |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2022.3196040 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | Change detection for remote sensing images involves detecting regional surface changes of interest between two images taken of the same geographical area but at different times. In image processing, the spatial domain uses grayscale values to describe an image. The frequency is directly related to the spatial change rate, so the frequency domain can be intuitively associated with patterns of intensity variations in the image. These two domains provide different perspectives for image interpretation. Most existing deep-learning-based methods formulate change detection as a pixel-wise binary classification problem and utilize various strategies to extract information in the spatial domain. However, they rarely pay attention to the rich information in the frequency domain. To address this problem, we propose an end-to-end joint frequency-spatial domain network (JFSDNet) to implement remote sensing optical image change detection. Specifically, we introduce frequency information into the change detection to supplement the loss of image details caused by downsampling. In addition, we employ a frequency selection module to adaptively discriminate and choose frequency clues by reducing the complexity of the frequency features. The JFSDNet is applied to two publicly available datasets: the change-detection dataset (CDD) dataset and the LEarning VIsion and Remote sensing Change Detection (LEVIR-CD) dataset. Compared with other methods, both visual interpretation and quantitative assessment confirmed that our proposed method achieved a favorable performance. |
| 资助项目 | Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao)[2022QNLM050301-2] ; National Natural Science Foundation of China[U2006211] ; National Natural Science Foundation of China[62171320] ; National Key Research and Development Program of China[2020YFC1523200] ; Major Projects of the National Natural Science Foundation of China[42090044] ; Chinese Academy of Science Program[Y9KY04101L] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000843314100026 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/179913]  |
| 专题 | 中国科学院海洋研究所 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Yuan,Feng, Yanjie,Huo, Shuwei,et al. Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:14. |
| APA | Zhou, Yuan,Feng, Yanjie,Huo, Shuwei,&Li, Xiaofeng.(2022).Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,14. |
| MLA | Zhou, Yuan,et al."Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):14. |
入库方式： OAI收割
来源：海洋研究所
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