Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China
With the widespread application of deep learning in Earth observation, remote sensing image-based building change detection has achieved numerous groundbreaking advancements. However, differences across time periods caused by temporal variations in land cover, as well as the complex spatial structur...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2249 |
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| author | Lei Fu Yunfeng Zhang Keyun Zhao Lulu Zhang Ying Li Changjing Shang Qiang Shen |
| author_facet | Lei Fu Yunfeng Zhang Keyun Zhao Lulu Zhang Ying Li Changjing Shang Qiang Shen |
| author_sort | Lei Fu |
| collection | DOAJ |
| description | With the widespread application of deep learning in Earth observation, remote sensing image-based building change detection has achieved numerous groundbreaking advancements. However, differences across time periods caused by temporal variations in land cover, as well as the complex spatial structures in remote sensing scenes, significantly constrain the performance of change detection. To address these challenges, a change detection algorithm based on spatio-spectral information aggregation is proposed, which consists of two key modules: the Cross-Scale Heterogeneous Convolution module (CSHConv) and the Spatio-Spectral Information Fusion module (SSIF). CSHConv mitigates information loss caused by scale heterogeneity, thereby enhancing the effective utilization of multi-scale features. Meanwhile, SSIF models spatial and spectral information jointly, capturing interactions across different spatial scales and spectral domains. This investigation is illustrated with a case study conducted with the real-world dataset QL-CD (Qinling change detection), acquired in the Qinling region of China. The work includes the construction of QL-CD, which includes 12,724 pairs of images captured by the Gaofen-1 satellite. Experimental results demonstrate that the proposed approach outperforms a wide range of state-of-the-art algorithms. |
| format | Article |
| id | doaj-art-a871566341f74a7f86159e4dbc70f1f7 |
| institution | Matheson Library |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a871566341f74a7f86159e4dbc70f1f72025-07-11T14:42:30ZengMDPI AGRemote Sensing2072-42922025-06-011713224910.3390/rs17132249Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in ChinaLei Fu0Yunfeng Zhang1Keyun Zhao2Lulu Zhang3Ying Li4Changjing Shang5Qiang Shen6School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, ChinaShaanxi Satellite Application Center for Natural Resources, Xi’an 710065, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an 710129, ChinaDepartment of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UKDepartment of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UKWith the widespread application of deep learning in Earth observation, remote sensing image-based building change detection has achieved numerous groundbreaking advancements. However, differences across time periods caused by temporal variations in land cover, as well as the complex spatial structures in remote sensing scenes, significantly constrain the performance of change detection. To address these challenges, a change detection algorithm based on spatio-spectral information aggregation is proposed, which consists of two key modules: the Cross-Scale Heterogeneous Convolution module (CSHConv) and the Spatio-Spectral Information Fusion module (SSIF). CSHConv mitigates information loss caused by scale heterogeneity, thereby enhancing the effective utilization of multi-scale features. Meanwhile, SSIF models spatial and spectral information jointly, capturing interactions across different spatial scales and spectral domains. This investigation is illustrated with a case study conducted with the real-world dataset QL-CD (Qinling change detection), acquired in the Qinling region of China. The work includes the construction of QL-CD, which includes 12,724 pairs of images captured by the Gaofen-1 satellite. Experimental results demonstrate that the proposed approach outperforms a wide range of state-of-the-art algorithms.https://www.mdpi.com/2072-4292/17/13/2249building change detectionspatio-spectral information fusioncross-scale heterogeneous convolutionbuilding change detection dataset |
| spellingShingle | Lei Fu Yunfeng Zhang Keyun Zhao Lulu Zhang Ying Li Changjing Shang Qiang Shen Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China Remote Sensing building change detection spatio-spectral information fusion cross-scale heterogeneous convolution building change detection dataset |
| title | Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China |
| title_full | Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China |
| title_fullStr | Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China |
| title_full_unstemmed | Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China |
| title_short | Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China |
| title_sort | remote sensing image based building change detection a case study of the qinling mountains in china |
| topic | building change detection spatio-spectral information fusion cross-scale heterogeneous convolution building change detection dataset |
| url | https://www.mdpi.com/2072-4292/17/13/2249 |
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