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NaGAN: Nadir-like Generative Adversarial Network for Off-Nadir Object Detection of Multi-View Remote Sensing Imagery
| Content Provider | MDPI |
|---|---|
| Author | Ni, Lei Huo, Chunlei Zhang, Xin Wang, Peng Zhang, Luyang Guo, Kangkang Zhou, Zhixin |
| Copyright Year | 2022 |
| Description | Detecting off-nadir objects is a well-known challenge in remote sensing due to the distortion and mutable representation. Existing methods mainly focus on a narrow range of view angles, and they ignore broad-view pantoscopic remote sensing imagery. To address the off-nadir object detection problem in remote sensing, a new nadir-like generative adversarial network (NaGAN) is proposed in this paper by narrowing the representation differences between the off-nadir and nadir object. NaGAN consists of a generator and a discriminator, in which the generator learns to transform the off-nadir object to a nadir-like one so that they are difficult to discriminate by the discriminator, and the discriminator competes with the generator to learn more nadir-like features. With the progressive competition between the generator and discriminator, the performances of off-nadir object detection are improved significantly. Extensive evaluations on the challenging SpaceNet benchmark for remote sensing demonstrate the superiority of NaGAN to the well-established state-of-the-art in detecting off-nadir objects. |
| Starting Page | 975 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14040975 |
| Journal | Remote Sensing |
| Issue Number | 4 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-16 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Multi-view Remote Sensing Imagery Object Detection Generative Adversarial Network Off-nadir Spacenet |
| Content Type | Text |
| Resource Type | Article |
