Name
Title | ||
|---|---|---|
Diverse sample generation with multi-branch conditional generative adversarial network for remote sensing objects detection |
Abstract | ||
|---|---|---|
The remote sensing data is difficult to collect and lack of diversity, which extremely limits the performance of object detection on remote sensing images. In this paper, a multi-branch conditional generative adversarial network (MCGAN) is proposed to augment data for object detection in optical remote sensing images, which is the first GANs-based data augmentation framework proposed for this topic. We use MCGAN to generate the diverse objects based on the existing remote sensing datasets. The multi-branch dilated convolution and the classification branch are adopted into MCGAN to help the generator to generate the diverse and high-quality images. Meanwhile, an adaptive samples selection strategy based on the Faster R-CNN is proposed to select the samples for data augmentation from the objects generated by MCGAN, which can ensure the quality of new augmented training sets and improve the diversity of samples. Experiments based on NWPU VHR-10 and DOTA show that the objects generated by MCGAN have the higher quality compared with the objects generated by WGAN and LSGAN. And the mean average precision detected by the state-of-the-art object detection models used in the experiments has the satisfactory improvement after the MCGAN based data augmentation, which indicates that data augmentation by MCGAN can effectively improve the accuracy of remote sensing images object detection. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.10.065 | Neurocomputing |
Keywords | Field | DocType |
Remote sensing,Deep learning,Object detection,Generative adversarial network,Data augmentation | Object detection,Generative adversarial network,Pattern recognition,Convolution,Remote sensing,Artificial intelligence,Mathematics | Journal |
Volume | ISSN | Citations |
381 | 0925-2312 | 3 |
PageRank | References | Authors |
0.44 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongjun Zhu | 1 | 4 | 2.50 |
| Shixiong Xia | 2 | 102 | 13.28 |
| Jiaqi Zhao | 3 | 117 | 15.77 |
| Yong Zhou | 4 | 61 | 12.72 |
| Meng Jian | 5 | 18 | 10.79 |
| Qiang Niu | 6 | 8 | 4.59 |
| Rui Yao | 7 | 28 | 12.75 |
| Ying Chen | 8 | 115 | 16.65 |