Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR
Change detection is a crucial remote sensing (RS) application because it can locate the interesting changed regions and provide corresponding time-series information with multitemporal RS images acquired in the same region. Synthetic aperture radar (SAR), with the advantages of all-day and all-weath...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10483102/ |
| _version_ | 1797200992716980224 |
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| author | Jinsong Zhang Mengdao Xing Wenkang Liu Guangcai Sun |
| author_facet | Jinsong Zhang Mengdao Xing Wenkang Liu Guangcai Sun |
| author_sort | Jinsong Zhang |
| collection | DOAJ |
| description | Change detection is a crucial remote sensing (RS) application because it can locate the interesting changed regions and provide corresponding time-series information with multitemporal RS images acquired in the same region. Synthetic aperture radar (SAR), with the advantages of all-day and all-weather conditions, can achieve high-resolution imaging from long operation distances. Moreover, the coherence imaging characteristics of the SAR system cause the attained complex-value images to be more sensitive to ground surface features. Traditional intensity-based change detection methods merely use the intensity difference between multitemporal images; thus, the results only reflect the significant landmark changes, such as seismic disasters and flood disasters. In this article, we use the coherent imaging characteristics of the SAR complex images to detect very subtle changes, such as vehicle tracks and footprints. Specifically, coherent change detection based on amplitude and phase generates a difference image utilizing the repeat-pass repeat-geometry SAR images, while the global attention-based convolutional neural network achieves automatic subtle change track extraction from the difference images. The experimental results based on our measured airborne SAR data demonstrate that our proposed method reduces the sensitivity of the detectable changed region from the meter level to the centimeter level, further providing our method with the ability to detect very subtle changes, such as vehicle tire tracks or footprints by human activities. This subtle change detection capability can be used for the search and rescue of vehicles and personnel lost in the field. |
| first_indexed | 2024-04-24T07:40:28Z |
| format | Article |
| id | doaj.art-b14f9b4620d9480fbd042874ead1946c |
| institution | Directory Open Access Journal |
| issn | 1939-1404 2151-1535 |
| language | English |
| last_indexed | 2024-04-24T07:40:28Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-b14f9b4620d9480fbd042874ead1946c2024-04-19T23:00:06ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-01178324833810.1109/JSTARS.2024.338263410483102Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SARJinsong Zhang0https://orcid.org/0000-0002-1004-7721Mengdao Xing1https://orcid.org/0000-0002-4084-0915Wenkang Liu2https://orcid.org/0000-0002-1004-7721Guangcai Sun3https://orcid.org/0000-0002-6482-0863Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, ChinaAcademy of Advanced Interdisciplinary Research, Xidian University, Xi'an, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, ChinaChange detection is a crucial remote sensing (RS) application because it can locate the interesting changed regions and provide corresponding time-series information with multitemporal RS images acquired in the same region. Synthetic aperture radar (SAR), with the advantages of all-day and all-weather conditions, can achieve high-resolution imaging from long operation distances. Moreover, the coherence imaging characteristics of the SAR system cause the attained complex-value images to be more sensitive to ground surface features. Traditional intensity-based change detection methods merely use the intensity difference between multitemporal images; thus, the results only reflect the significant landmark changes, such as seismic disasters and flood disasters. In this article, we use the coherent imaging characteristics of the SAR complex images to detect very subtle changes, such as vehicle tracks and footprints. Specifically, coherent change detection based on amplitude and phase generates a difference image utilizing the repeat-pass repeat-geometry SAR images, while the global attention-based convolutional neural network achieves automatic subtle change track extraction from the difference images. The experimental results based on our measured airborne SAR data demonstrate that our proposed method reduces the sensitivity of the detectable changed region from the meter level to the centimeter level, further providing our method with the ability to detect very subtle changes, such as vehicle tire tracks or footprints by human activities. This subtle change detection capability can be used for the search and rescue of vehicles and personnel lost in the field.https://ieeexplore.ieee.org/document/10483102/Coherence change detection (CCD)convolutional neural network (CNN)synthetic aperture radar (SAR)UNet |
| spellingShingle | Jinsong Zhang Mengdao Xing Wenkang Liu Guangcai Sun Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coherence change detection (CCD) convolutional neural network (CNN) synthetic aperture radar (SAR) UNet |
| title | Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR |
| title_full | Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR |
| title_fullStr | Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR |
| title_full_unstemmed | Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR |
| title_short | Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR |
| title_sort | joint exploitation of coherent change detection and global context capturing network for subtle changed track detection with airborne sar |
| topic | Coherence change detection (CCD) convolutional neural network (CNN) synthetic aperture radar (SAR) UNet |
| url | https://ieeexplore.ieee.org/document/10483102/ |
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