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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >High-precision Registration Algorithm and Parallel Design Method for High-Resolution Optical Remote Sensing Images
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High-precision Registration Algorithm and Parallel Design Method for High-Resolution Optical Remote Sensing Images

机译：高分辨率注册算法及高分辨率光学遥感图像的并行设计方法

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摘要

In optical remote sensing image reconstruction, image registration is an important issue to address in order to ensure satisfactory reconstruction performance. In this study, a multi-frame image registration algorithm for high-resolution images and its parallel design method are proposed. The algorithm realizes an improved feature point detection method based on an adaptive gradient bilateral tensor filter and carries out weighted Gaussian surface sub-pixel interpolation to obtain more accurate corner positions, which better guarantees the registration accuracy. On this basis, multi-scale expansion is carried out to generate descriptors for image registration. In addition, the operation-level parallel analysis and design are carried out on a GPU platform based on compute unified device architecture (CUDA), and the memory model of the GPU is utilized reasonably. The task-level parallel analysis and design are carried out based on the GPU stream model. Moreover, based on the open multi-processing (OpenMP) platform, a multi-core CPU carries out parallel design at the operation level and task level, which realizes post-processing operations such as optical remote sensing images loading, accurate matching, and coordinate mapping, thereby effectively improving registration speed. Compared with feature point algorithms and deep learning algorithm, our algorithm and its parallel design significantly improve the registration accuracy and speed of high-resolution optical remote sensing images.
机译：在光学遥感图像重建中，图像配准是解决的重要问题，以确保令人满意的重建性能。在该研究中，提出了一种用于高分辨率图像及其并行设计方法的多帧图像配准算法。该算法实现了基于自适应梯度双侧张滤波器的改进的特征点检测方法，并进行加权高斯表面子像素插值，以获得更精确的角位置，这更好地保证了登记精度。在此基础上，执行多尺度扩展以生成用于图像配准的描述符。另外，在基于计算统一设备架构（CUDA）的GPU平台上执行操作级并行分析和设计，并且GPU的存储器模型合理地使用。基于GPU流模型执行任务级并行分析和设计。此外，基于开放式多处理（OpenMP）平台，多核CPU在操作级别和任务级别进行并行设计，这实现了后处理操作，例如光学遥感图像加载，准确匹配和坐标。映射，从而有效提高登记速度。与特征点算法和深度学习算法相比，我们的算法及其并行设计显着提高了高分辨率光遥感图像的登记精度和速度。

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