基于生成对抗网络的机载遥感图像超分辨率重建

为解决机载遥感图像质量易受环境影响的问题，对其进行超分辨率重建，对现有深度学习机载遥感图像超分辨率重建方法存在的特征提取能力差、重建图像边缘平滑、模型训练困难的问题进行改进，增强图像重建效果。将生成对抗网络作为模型的整体框架，使用密集剩余残差块增强模型特征提取能力，增加跳跃连接，有效提取机载遥感图像的浅层和深层特征，引入沃瑟斯坦式生成对抗网络优化模型训练。该方法能够有效对机载遥感图像进行4倍重建，在峰值信噪比评价上较对比方法约有2 dB增益，重建出的机载遥感图像在视觉上更清晰、细节更丰富、边缘更锐利。实验结果表明，该方法有效提升了模型特征提取能力，优化了训练过程，重建的机载遥感图像效果较好。

Super-resolution reconstruction of airborne remote sensing images based on the generative adversarial networks

To solve the problem that the quality of airborne remote sensing images is susceptible to environmental impacts, super-resolution reconstruction is carried out. The existing super-resolution reconstruction methods for deep learning airborne remote sensing images has the problems of poor feature extraction capability, smooth edges of reconstructed images and difficulty in model training, the image reconstruction effect is enhanced to solve the above problems. The generative adversarial network is taken as the overall framework of the model. The dense residual block is used to enhance the feature extraction capability of the model, and jump connection is added to effectively extract the shallow and deep features of airborne remote sensing images. The Wasserstein-type generative adversarial network optimization model training is introduced. The method can effectively reconstruct airborne remote sensing images by 4 times, and has a gain of 2 dB or so in peak signal-to-noise ratio evaluation compared with other methods for comparison. The reconstructed airborne remote sensing images are clearer in vision, richer in details and sharper in edges. The experimental results show that the method effectively improves the model feature extraction ability, optimizes the training process, and the reconstructed airborne remote sensing image has better effect.