基于增强生成对抗网络的滑坡影像集超分辨率重建

针对高分遥感影像用于滑坡智能识别缺少高清训练集的问题，在组建高清滑坡训练集过程中，为充分利用低分辨率滑坡影像，本文采用基于增强型生成对抗网络模型(ESRGAN)实现了低分辨率滑坡影像集超分辨率重建。ESRGAN模型在SRGAN模型基础上，通过移除批归一化层、加入多级残差网络与残差缩放系数，提升了生成器的特征提取性能与稳定性，并采用迁移学习方法，基于毕节滑坡影像集与云南南景高速公路滑坡影像集进行试验验证。试验结果表明，基于迁移学习的ESRGAN模型在峰值信噪比(PSNR)与结构相似性(SSIM)方面获得更高得分，超分辨率重建取得更优结果。本文研究结果为获取滑坡高分辨率遥感影像集提供了一种新的技术方法。

Super-resolution reconstruction of landslide image set based on enhanced generation countermeasure network

To address the problem of the lack of high-resolution training set in the use of high-resolution remote sensing images for landslide intelligent recognition. In the process of forming the high-resolution landslide training set, in order to make full use of the previous low-resolution landslide images, this paper adopts enhanced super-resolution generative adversarial networks(ESRGAN) to achieve super-resolution reconstruction of the low-resolution landslide image set. The ESRGAN model improves the feature extraction performance and stability of the generator by removing the batch normalization layer, adding multi-stage residual network and residual scaling factor based on the SRGAN model, and using the transfer learning method to conduct experimental based on the Bijie landslide image set and Yunnan Nanjing highway landslide image set. The experimental results show that the ESRGAN model based on transfer learning can achieve higher scores in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and better results in super-resolution reconstruction. The results of this paper can provide a new technical method for acquiring high-resolution remote sensing image sets of landslides.