关键词: 遥感图像, 超分辨率, 边界平衡生成对抗网络, 自编码器, 重构误差

Abstract: Due to the limitation of imaging devices and transmission conditions, the definition of remote sensing image is difficult to guarantee. Image super-resolution technology aims at recovering high-resolution images from low-resolution images, which is of great significance for high quality interpretation of remote sensing images. In this paper, a single remote sensing image super-resolution method based on boundary equilibrium generative adversarial networks is proposed to solve the problems of traditional methods depending on the multi-frame image sequence and the reconstruction results are too smooth. Both the generator and the discriminator are designed as end to end autoencoder with skip connections. In order to enhance the quality of the generated images and accelerate the convergence of the network, a loss function based on the discriminator reconstruction error is used. The experimental results on the NWPU-RESISC45 dataset show that the proposed method can provide more high frequency information, and the reconstruction results are closest to the real image. Compared with the nearest interpolation and the bicubic interpolation methods, the PSNR is raised about 2.70 dB. Compared with other methods based on the deep convolution neural network, the PSNR is improved by about 0.72 dB.

Key words: remote sensing image, super-resolution, boundary equilibrium generative adversarial network, auto-encoder, reconstruction error