增强型遥感影像SRGAN算法及其在三维重建精度提升中的应用

遥感影像是地形测绘、三维重建等任务的主要数据源之一,分辨率影响着被测目标在影像上的表示能力,对后期三维模型的定位精度及重建效果起着重要作用。针对遥感影像像幅较大且目标特征表现复杂、细节丰富的特点,结合实景三维模型重建的需求,提出了一种增强型遥感影像SRGAN算法。克服了传统方法进行超分重建时易出现边缘效应、产生模糊重建的情况,改进了简单卷积网络仅能提取影像中较为浅层的特征信息,无法在提高分辨率的同时保留影像丰富细节的局限。本文所提算法在生成模型中使用密集剩余残差块进行深层特征提取,在判别模型中引入多尺度判别思想,从而保证遥感影像重建时特征纹理、细节信息、高频目标的完整与精确。实验构建不同时间、不同类型区域的遥感影像数据集,在此基础上将本文算法与Bicubic、SRGAN、ESRGAN算法进行对比分析,在超分重建中PSNR较对比算法提升约3个单位,渗透指数PI更趋向且稳定于1,SSIM与清晰度指标Q同样得到较好改善;在三维重建中影像密集匹配点数量得到提升,同时误差减少,模型精细程度和定位精度得到提高。结果表明,本文算法适用于遥感影像超分辨率重建问题,并在实景三维模型重建中对精度的提升表现较好。

Enhanced Remote Sensing Image SRGAN Algorithm and Its Application in Improving the Accuracy of 3D Reconstruction

Remote sensing images are important data sources for terrain mapping, 3D reconstruction, and other tasks. The spatial resolution of remote sensing images determines the representation ability of the measured object on the image and plays an important role in the positioning accuracy and reconstruction effect of 3D model in the later stage. In view of the characteristics of high resolution remote sensing images including large scale, complex target features, and rich details, an enhanced SRGAN algorithm for remote sensing image reconstruction is proposed to meet the needs of 3D model reconstruction. The proposed algorithm overcomes the problems of edge effect and fuzzy reconstruction using traditional methods for super-resolution reconstruction. In traditional methods, there is limitation that simple convolutional networks can only extract the shallow feature information of the image and cannot retain the rich details of the image with the increasing resolution. The proposed algorithm is based on the generative adversarial networks using deep learning, in which dense residual blocks are used to extract deep features, and multi-scale discrimination is introduced into the discriminant model. In the training, the generation model and the discrimination model learn features together and are optimized to finally obtain a super-resolution reconstruction model suitable for remote sensing image application. This model can improve the resolution and image quality of remote sensing images, and ensure the integrity and accuracy of feature texture, detail information, and high-frequency target. In our study, the proposed algorithm is compared with the Bicubic, SRGAN, and ESRGAN algorithms. Our results show that the PSNR of the proposed algorithm is improved by about three units, the Penetration Index (PI) is stable and closer to one, and the SSIM and clarity index Q are also improved. In 3D reconstruction, the number of image dense matching points is increased, and the error is reduced. The measured point values of the model are closer to the measured point values from the field. The visual perception of the model is also more real and delicate, which indicates that the precision and positioning accuracy of the 3D model can be significantly improved using the remote sensing images constructed by the proposed algorithm. The results demonstrate the proposed algorithm that considers the characteristics of remote sensing images performs better than other algorithms for the super-resolution reconstruction, and the geometric accuracy and visual accuracy of the real 3D models based on the constructed images are also significantly improved.