We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
NaGAN: Nadir-like Generative Adversarial Network for Off-Nadir Object Detection of Multi-View Remote Sensing Imagery.
- Authors
Ni, Lei; Huo, Chunlei; Zhang, Xin; Wang, Peng; Zhang, Luyang; Guo, Kangkang; Zhou, Zhixin
- Abstract
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.
- Subjects
GENERATIVE adversarial networks; REMOTE sensing; OBJECT recognition (Computer vision)
- Publication
Remote Sensing, 2022, Vol 14, Issue 4, p975
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs14040975