遥感图像是露天矿区生产的主要信息源之一，其空间分辨率影响着矿区各场景边界的区分、细小地物的判读和控制点的定位等，对矿区的生产管理与监测起着重要作用，针对现实中受成本、技术等制约，获取不了满足要求的高空间分辨率图像的问题，提出了利用超分辨率重建技术提高露天矿区遥感图像的空间分辨率;针对露天矿区各场景纹理特征明显的特点，采用深度纹理转移的方法进行超分辨率重建。通过一个端到端的深度学习模型，输入低分辨率的图像和对应的参考图像，把其分成若干个图像块，利用特征提取网络提取图像块的特征，并比较低分辨率特征图像块和参考特征图像块纹理相似性，自适应地从参考图像中转移纹理，在各种尺度的特征层中把多个交换的纹理特征图融合到生成网络中，构建纹理细节丰富的重建图像。同时对特征提取部分进行了改进，用网络深度更深，运算量更小的ResNet34网络代替VGG19网络，更进一步提高了特征提取的效果。研究利用自制的露天矿区图像数据集进行实验，并与先进的图像超分辨率重建方法比较。研究结果表明:在参考图像对结果的影响方面，改进的方法重建效果会随参考图像与待重建图像相似度的增加而提高;与其他方法对比表明改进的方法在峰值信噪比、结构相似性等方面的值都优于未改进的方法、EDSR和SRGAN等方法，视觉感知方面也优于其他方法。

Remote sensing image is one of the main sources in open-pit mines production. Its spatial resolution affects the identification of the boundary of each scene in mining area,the interpretation of small ground objects and the loca- tion of control points. Remote sensing images play an important role in the open-pit production management and moni- toring. Aiming at the problem of high spatial resolution image that meets the restrictions,such as cost and technological constraints,it is proposed to use a super-resolution technology to improve the spatial resolution of remote sensing ima- ges in open-pit mining areas. According to the obvious texture features of each scene in the open-pit mining area,the research used a deep learning texture transfer super-resolution method. Through an end-to-end deep learning model, the processes are as follows:input low-resolution images and corresponding reference images,divide them into several image patches,extract the features of image patches using feature extraction network,and compare low-resolution fea- ture image blocks and reference features. The image patches texture similarity adaptively transfers the texture from ref- erence images,and fuses the plurality of ex-changed texture feature maps into the generation network in the feature layers of various scales to construct the reconstructed images with rich texture details. At the same time,the ResNet34 with deeper network depth and smaller computing capacity is used to replace the VGG19,which further improves the feature extraction effect. The research used a self-made open-pit mining area dataset for experiments. Compared with the advanced image super-resolution methods,in terms of the influence of the reference image on the results,the re- sults show that the improved method reconstruction effect will increase with the similarity between reference images and input images. Compared with other methods,the values of peak signal-to-noise ratio and structural similarity are better than the original methods,EDSR and SRGAN,and the visual perception is better.