NaGAN: Nadir-like Generative Adversarial Network for Off-Nadir Object Detection of Multi-View Remote Sensing Imagery

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-02, Vol.14 (4), p.975
• Main Authors: Ni, Lei, Huo, Chunlei, Zhang, Xin, Wang, Peng, Zhang, Luyang, Guo, Kangkang, Zhou, Zhixin
• Format: Article
• Language: English
• Subjects: generative adversarial network; Generative adversarial networks; Methods; multi-view remote sensing imagery; Neural networks; object detection; Object recognition; off-nadir; Remote sensing; Representations; Satellites; SpaceNet
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14040975

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.