EML-GAN: Generative Adversarial Network-Based End-to-End Multi-Task Learning Architecture for Super-Resolution Reconstruction and Scene Classification of Low-Resolution Remote Sensing Imagery
Weihuan Deng, Qiqi Zhu, Xiongli Sun, Weihua Lin, Qingfeng Guan · 2021
High spatial resolution remote sensing images (HSR-RSIs) are critical to providing fine land cover/land use information for scene classification. The global low spatial resolution remote sensing images (LSR-RSIs) can be easily obtained at present, whereas it is still a challenge to acquire large-scale HSR-RSIs. In this paper, an algorithmic-based architecture is proposed to improve the spatial resolution of RSIs beyond the limits of imaging sensors. The generative adversarial network-based end-to-end multi-task learning architecture (EML-GAN) is proposed for LSR-RSIs super-resolution reconstruction and scene classification simultaneously. In EML-GAN, the generator network is used to recover the fine geometric structures of LSR-RSIs by fusing the deep contextual, structure, and edge information. In addition, the discriminator network is designed to predict the scene label and distinguish the real/fake of the input data. The proposed architecture is evaluated on a public dataset and two self-made dataset. The experimental results show that the proposed architecture improves the visual effect and classification performance of LSR-RSIs.