High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images
Ship recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study ha...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2022.970832/full |
| _version_ | 1811305511404961792 |
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| author | Hongtu Xie Xinqiao Jiang Xiao Hu Zhitao Wu Guoqian Wang Kai Xie |
| author_facet | Hongtu Xie Xinqiao Jiang Xiao Hu Zhitao Wu Guoqian Wang Kai Xie |
| author_sort | Hongtu Xie |
| collection | DOAJ |
| description | Ship recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study has proposed a high-efficiency and low-energy ship recognition strategy based on the spiking neural network (SNN) in the SAR images. First, the visual attention mechanism is used to extract the visual saliency map from the SAR image, and then the Poisson encoder is used to encode it into a spike train, which can suppress the background noise while retaining the visual saliency feature of the SAR image. Besides, an SNN model integrating the time-series information is constructed by combining the leaked and integrated firing spiking neurons with the convolutional neural network (CNN), which can use the firing frequency of the spiking neurons to realize the ship recognition in SAR images. Finally, to solve the problem that SNN model is difficult to train, the arctangent function is used as the surrogate gradient function of the spike emission function during the backpropagation. Hence, applying this backpropagation method to the training process can optimize the SNN model. The experimental results show the following: (1) the proposed strategy can more accurately recognize the ship in the SAR image, and the F1 score can reach 98.55%, which has a better recognition performance than the other traditional deep learning methods; (2) the proposed strategy has the least amount of model parameters (only 3.11MB), which is far less than the model parameters of the other traditional deep learning methods; (3) the proposed strategy has fewer operations (only 17.97M) and can reach 1/30 time of operands of the other traditional deep learning methods, which shows the high efficiency of the proposed strategy using the spike emission signals; (4) the proposed strategy has the energy loss of 1.38 × 10−6J, which can achieve the low energy advantage of nearly three orders of the magnitude compared to the other traditional deep learning methods, indicating that the proposed strategy has a significant energy efficiency. |
| first_indexed | 2024-04-13T08:26:40Z |
| format | Article |
| id | doaj.art-3ce284c3c0434047a8d5a7236e9b7caf |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-04-13T08:26:40Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-3ce284c3c0434047a8d5a7236e9b7caf2022-12-22T02:54:24ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182022-09-011610.3389/fnbot.2022.970832970832High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR imagesHongtu Xie0Xinqiao Jiang1Xiao Hu2Zhitao Wu3Guoqian Wang4Kai Xie5School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaThe Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, ChinaSchool of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaShip recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study has proposed a high-efficiency and low-energy ship recognition strategy based on the spiking neural network (SNN) in the SAR images. First, the visual attention mechanism is used to extract the visual saliency map from the SAR image, and then the Poisson encoder is used to encode it into a spike train, which can suppress the background noise while retaining the visual saliency feature of the SAR image. Besides, an SNN model integrating the time-series information is constructed by combining the leaked and integrated firing spiking neurons with the convolutional neural network (CNN), which can use the firing frequency of the spiking neurons to realize the ship recognition in SAR images. Finally, to solve the problem that SNN model is difficult to train, the arctangent function is used as the surrogate gradient function of the spike emission function during the backpropagation. Hence, applying this backpropagation method to the training process can optimize the SNN model. The experimental results show the following: (1) the proposed strategy can more accurately recognize the ship in the SAR image, and the F1 score can reach 98.55%, which has a better recognition performance than the other traditional deep learning methods; (2) the proposed strategy has the least amount of model parameters (only 3.11MB), which is far less than the model parameters of the other traditional deep learning methods; (3) the proposed strategy has fewer operations (only 17.97M) and can reach 1/30 time of operands of the other traditional deep learning methods, which shows the high efficiency of the proposed strategy using the spike emission signals; (4) the proposed strategy has the energy loss of 1.38 × 10−6J, which can achieve the low energy advantage of nearly three orders of the magnitude compared to the other traditional deep learning methods, indicating that the proposed strategy has a significant energy efficiency.https://www.frontiersin.org/articles/10.3389/fnbot.2022.970832/fullship target recognitionsynthetic aperture radar (SAR)SAR imagespiking neural network (SNN)high-efficiencylow-energy |
| spellingShingle | Hongtu Xie Xinqiao Jiang Xiao Hu Zhitao Wu Guoqian Wang Kai Xie High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images Frontiers in Neurorobotics ship target recognition synthetic aperture radar (SAR) SAR image spiking neural network (SNN) high-efficiency low-energy |
| title | High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images |
| title_full | High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images |
| title_fullStr | High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images |
| title_full_unstemmed | High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images |
| title_short | High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images |
| title_sort | high efficiency and low energy ship recognition strategy based on spiking neural network in sar images |
| topic | ship target recognition synthetic aperture radar (SAR) SAR image spiking neural network (SNN) high-efficiency low-energy |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2022.970832/full |
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