Name
Abstract | ||
|---|---|---|
At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time-space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13163192 | REMOTE SENSING |
Keywords | DocType | Volume |
ship detection, remote sensing, active rotating filter, channelwise attention, spatial attention | Journal | 13 |
Issue | Citations | PageRank |
16 | 1 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxin Dong | 1 | 1 | 0.68 |
| Fukun Chen | 2 | 1 | 1.36 |
| Shuang Han | 3 | 1 | 0.34 |
| Hao Liu | 4 | 1 | 0.34 |