Name
Abstract | ||
|---|---|---|
Deep learning has been widely used in the field of SAR ship detection. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets. In order to promote the solution to the above problems, this article releases a high-resolution SAR ship detection dataset which can be used for rotating frame target detection. The dataset contains six categories of ships. In total, 30 panoramic SAR tiles of the Chinese Gaofen-3 of port areas with a 1-m resolution were cropped to slices, each with 1024 x 1024 pixels. In addition, most of the images in the dataset contain nearshore areas with complex background interference. Eight state-of-the-art rotated detectors and a CFAR-based method were used to evaluate the dataset. Experimental results revealed that the complex background will have a great impact on the performance of detectors. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13245104 | REMOTE SENSING |
Keywords | DocType | Volume |
ship detection dataset, high-resolution SAR, rotating frame target detection | Journal | 13 |
Issue | Citations | PageRank |
24 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songlin Lei | 1 | 0 | 0.68 |
| Dongdong Lu | 2 | 0 | 0.34 |
| Xiaolan Qiu | 3 | 1 | 2.72 |
| Chibiao Ding | 4 | 223 | 33.52 |