Self-supervised change detection of heterogeneous images based on difference algorithms
The presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2024.2372854 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846127708468150272 |
|---|---|
| author | Jinsha Wu Shuwen Yang Yikun Li Yukai Fu Zhuang Shi Yao Zheng |
| author_facet | Jinsha Wu Shuwen Yang Yikun Li Yukai Fu Zhuang Shi Yao Zheng |
| author_sort | Jinsha Wu |
| collection | DOAJ |
| description | The presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and a simplified pulse-coupled neural network (PC-SPCNN) is used to fuse the heterogeneous images, and the result is used to compute the difference image (DI). The new DI generation method can generate the standard and exponential difference images. Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. Finally, the support vector machine classifier automatically detects whether the intermediate pixels are changed and produces the change detection results. The experimental results confirm the improvements made by the proposed method in difference image extraction, training sample selection, and clustering algorithm, and the stability of the method exceeds that of the state-of-the-art change detection methods. |
| format | Article |
| id | doaj-art-f3c1b9683e914ce588ebf8bd378c0404 |
| institution | Kabale University |
| issn | 2279-7254 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-f3c1b9683e914ce588ebf8bd378c04042024-12-11T11:43:31ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542024-12-0157110.1080/22797254.2024.2372854Self-supervised change detection of heterogeneous images based on difference algorithmsJinsha Wu0Shuwen Yang1Yikun Li2Yukai Fu3Zhuang Shi4Yao Zheng5Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaThe presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and a simplified pulse-coupled neural network (PC-SPCNN) is used to fuse the heterogeneous images, and the result is used to compute the difference image (DI). The new DI generation method can generate the standard and exponential difference images. Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. Finally, the support vector machine classifier automatically detects whether the intermediate pixels are changed and produces the change detection results. The experimental results confirm the improvements made by the proposed method in difference image extraction, training sample selection, and clustering algorithm, and the stability of the method exceeds that of the state-of-the-art change detection methods.https://www.tandfonline.com/doi/10.1080/22797254.2024.2372854Heterogeneous imageschange detectionself-supervised learningdifference imagehierarchical fuzzy c-means clustering |
| spellingShingle | Jinsha Wu Shuwen Yang Yikun Li Yukai Fu Zhuang Shi Yao Zheng Self-supervised change detection of heterogeneous images based on difference algorithms European Journal of Remote Sensing Heterogeneous images change detection self-supervised learning difference image hierarchical fuzzy c-means clustering |
| title | Self-supervised change detection of heterogeneous images based on difference algorithms |
| title_full | Self-supervised change detection of heterogeneous images based on difference algorithms |
| title_fullStr | Self-supervised change detection of heterogeneous images based on difference algorithms |
| title_full_unstemmed | Self-supervised change detection of heterogeneous images based on difference algorithms |
| title_short | Self-supervised change detection of heterogeneous images based on difference algorithms |
| title_sort | self supervised change detection of heterogeneous images based on difference algorithms |
| topic | Heterogeneous images change detection self-supervised learning difference image hierarchical fuzzy c-means clustering |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2024.2372854 |
| work_keys_str_mv | AT jinshawu selfsupervisedchangedetectionofheterogeneousimagesbasedondifferencealgorithms AT shuwenyang selfsupervisedchangedetectionofheterogeneousimagesbasedondifferencealgorithms AT yikunli selfsupervisedchangedetectionofheterogeneousimagesbasedondifferencealgorithms AT yukaifu selfsupervisedchangedetectionofheterogeneousimagesbasedondifferencealgorithms AT zhuangshi selfsupervisedchangedetectionofheterogeneousimagesbasedondifferencealgorithms AT yaozheng selfsupervisedchangedetectionofheterogeneousimagesbasedondifferencealgorithms |