Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNet
As a branch application of the synthetic aperture radar (SAR), coherent change detection (CCD) is commonly utilized to help differentiate specific changes from the unchanged scene. To exactly pick out manmade changes of interest from change maps, the introduction of artificial neural networks (ANNs)...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11050980/ |
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| author | Xinyi Ye Yanxing Liu Zihan Wang Yizhe Fan Bingchen Zhang |
| author_facet | Xinyi Ye Yanxing Liu Zihan Wang Yizhe Fan Bingchen Zhang |
| author_sort | Xinyi Ye |
| collection | DOAJ |
| description | As a branch application of the synthetic aperture radar (SAR), coherent change detection (CCD) is commonly utilized to help differentiate specific changes from the unchanged scene. To exactly pick out manmade changes of interest from change maps, the introduction of artificial neural networks (ANNs) has significantly enhanced detection performance but taken up great computational resources, making it challenging to deploy on edge devices with limited resources and a strict power budget. Spiking neural networks (SNNs) not only infer results more energy-efficiently but also have the potential to drive ultralow-power neuromorphic computers which ANNs lack. In this study, to detect coherent changes precisely and with less energy consumption, a robust SNN-based change analysis method is proposed to make it available to implement CCD tasks via the “WenTian I” brain-like computer. First, we adopt a Poisson spike encoding method based on fuzzy <italic>c</italic>-means clustering to preclassify pixels and translate continuous change maps into spikes. Second, we input the spike images into a novel deep SNN, multithreshold Spiking-UNet. By representing information in the form of sparse spike flows, our proposed change detection model can reduce the energy consumption of CCD while maintaining excellent detection capabilities. The corresponding experimental results validate the comparable detection effectiveness and superior energy efficiency of our SNN-based method used for SAR CCD. |
| format | Article |
| id | doaj-art-8a3a358336a64f76b66e96208a3a4437 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-8a3a358336a64f76b66e96208a3a44372025-08-20T03:50:38ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118171391715310.1109/JSTARS.2025.358305811050980Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNetXinyi Ye0https://orcid.org/0009-0009-0227-5482Yanxing Liu1https://orcid.org/0009-0007-8604-933XZihan Wang2https://orcid.org/0009-0007-3085-8621Yizhe Fan3https://orcid.org/0009-0005-7551-2275Bingchen Zhang4Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAs a branch application of the synthetic aperture radar (SAR), coherent change detection (CCD) is commonly utilized to help differentiate specific changes from the unchanged scene. To exactly pick out manmade changes of interest from change maps, the introduction of artificial neural networks (ANNs) has significantly enhanced detection performance but taken up great computational resources, making it challenging to deploy on edge devices with limited resources and a strict power budget. Spiking neural networks (SNNs) not only infer results more energy-efficiently but also have the potential to drive ultralow-power neuromorphic computers which ANNs lack. In this study, to detect coherent changes precisely and with less energy consumption, a robust SNN-based change analysis method is proposed to make it available to implement CCD tasks via the “WenTian I” brain-like computer. First, we adopt a Poisson spike encoding method based on fuzzy <italic>c</italic>-means clustering to preclassify pixels and translate continuous change maps into spikes. Second, we input the spike images into a novel deep SNN, multithreshold Spiking-UNet. By representing information in the form of sparse spike flows, our proposed change detection model can reduce the energy consumption of CCD while maintaining excellent detection capabilities. The corresponding experimental results validate the comparable detection effectiveness and superior energy efficiency of our SNN-based method used for SAR CCD.https://ieeexplore.ieee.org/document/11050980/Coherent change detection (CCD)fuzzy <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">c</italic>-means (FCM) clusteringPoisson spike encodingspiking neural network (SNN)synthetic aperture radar (SAR)U-Net |
| spellingShingle | Xinyi Ye Yanxing Liu Zihan Wang Yizhe Fan Bingchen Zhang Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNet IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coherent change detection (CCD) fuzzy <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">c</italic>-means (FCM) clustering Poisson spike encoding spiking neural network (SNN) synthetic aperture radar (SAR) U-Net |
| title | Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNet |
| title_full | Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNet |
| title_fullStr | Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNet |
| title_full_unstemmed | Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNet |
| title_short | Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNet |
| title_sort | energy efficient sar coherent change detection based on deep multithreshold spiking unet |
| topic | Coherent change detection (CCD) fuzzy <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">c</italic>-means (FCM) clustering Poisson spike encoding spiking neural network (SNN) synthetic aperture radar (SAR) U-Net |
| url | https://ieeexplore.ieee.org/document/11050980/ |
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