Loading...
Please wait, while we are loading the content...
Similar Documents
SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network
| Content Provider | MDPI |
|---|---|
| Author | Zhou, Fang He, Fengjie Gui, Changchun Dong, Zhangyu Xing, Mengdao |
| Copyright Year | 2022 |
| Description | A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets’ sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods. |
| Starting Page | 180 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14010180 |
| Journal | Remote Sensing |
| Issue Number | 1 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-01 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Synthetic Aperture Radar (sar) Target Detection Deep Learning Saliency Map |
| Content Type | Text |
| Resource Type | Article |
