Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening
Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening
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DOI：
10.3390/rs13183674
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发表时间：
2021-09
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影响因子：
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通讯作者：
Guangqi Xie;Mi Wang;Zhiqi Zhang;Shao Xiang;Luxiao He
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文献类型：
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作者：
Guangqi Xie;Mi Wang;Zhiqi Zhang;Shao Xiang;Luxiao He
提出了一种基于图形处理器（GPU）的高分辨率全色（PAN）和多光谱（MS）图像亚像素准实时自动配准方法。在第一步骤中，该方法使用差分地理配准来实现PAN和MS图像的精确地理配准。差分地理配准将PAN和MS图像标准化为相同的方向和规模。由于采集仪器的几何配置，还存在一些残余未对准。这些残余未对准意味着PAN和MS图像在差分地理配准之后仍然具有偏差。第二步是使用具有微小面基元的差分校正来消除可能的残余未对准。差分校正校正PAN和MS图像之间的相对内部几何失真。这两个步骤的计算负担很大，传统的中央处理器（CPU）处理需要很长时间。由于差分方法的天然并行性，这两个步骤非常适合映射到GPU上进行处理，在保证处理精度的同时实现近实时处理。本文利用高分6号、高分7号、资源3 -02号和SuperView-1卫星数据进行了试验。实验表明，该方法的处理精度在0.5像素以内。该方法在NVIDIA GeForce RTX 2080 Ti中对1 GB输出数据的自动处理时间约为2.5 s，可以满足大多数卫星的近实时处理要求。该方法可以快速实现PAN和MS图像的高精度配准。它适用于不同的场景和不同的传感器。它对PAN和MS之间的配准误差非常鲁棒。
This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS.