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| Citation: | LI Yi, DU Lan, and DU Yuang. Convolutional neural network based on feature decomposition for target detection in SAR images[J]. Journal of Radars, 2023, 12(5): 1069–1080. doi: 10.12000/JR23004
|
Convolutional Neural Network Based on Feature Decomposition for Target Detection in SAR Images
DOI: 10.12000/JR23004
More Information-
Abstract
Most high-resolution Synthetic Aperture Radar (SAR) images of real-life scenes are complex due to clutter, such as grass, trees, roads, and buildings, in the background. Traditional target detection algorithms for SAR images contain numerous false and missed alarms due to such clutter, adversely affecting the performance of SAR images target detection. Herein we propose a feature decomposition-based Convolutional Neural Network (CNN) for target detection in SAR images. The feature extraction module first extracts features from the input images, and these features are then decomposed into discriminative and interfering features using the feature decomposition module. Furthermore, only the discriminative features are input into the multiscale detection module for target detection. The interfering features that are removed after feature decomposition are the parts that are unfavorable to target detection, such as complex background clutter, whereas the discriminative features that are retained are the parts that are favorable to target detection, such as the targets of interest. Hence, an effective reduction in the number of false and missed alarms, as well as an improvement in the performance of SAR target detection, is achieved. The F1-score values of the proposed method are 0.9357 and 0.9211 for the MiniSAR dataset and SAR Aircraft Detection Dataset (SADD), respectively. Compared to the single shot multibox detector without the feature extraction module, the F1-score values of the proposed method for the MiniSAR and SADD datasets show an improvement of 0.0613 and 0.0639, respectively. Therefore, the effectiveness of the proposed method for target detection in SAR images of complex scenes was demonstrated through experimental results based on the measured datasets. -
References
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Wang Xuesong. Status and Prospects of Radar Polarimetry Techniques[J]. Journal of Radars, 2016, 5(2): 119-131. doi: 10.12000/JR16039
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- Figure 1. Flow chart of SAR target detection method based on feature decomposition CNN
- Figure 2. Structure of feature decomposition module
- Figure 3. Two SAR images in the MiniSAR dataset
- Figure 4. Examples in the SADD[23]
- Figure 5. Detection results of the first test image in the MiniSAR dataset by different methods
- Figure 6. Detection results of the second test image in the MiniSAR dataset by different methods
- Figure 7. Detection results of some test chips in the SADD by different methods
- Figure 8. The discriminative feature maps and interfering feature maps and reconstruction maps obtained by the feature decomposition module for some SAR sub-images