高分辨率遥感图像配准并行加速方法

Parallel Acceleration Method for Very High Resolution Remote Sensing Image Registration

基于SIFT算法的遥感图像配准精度高、稳定性强,但图像幅宽大、提取特征点数量多使得配准过程耗时长。提出了一种高分辨率遥感图像配准的并行加速方法。该方法在特征点提取时利用GPU实现了高斯金字塔建立过程中的并行加速,并对提取出的大量特征点使用共享内存来进行局部极值高速缓存,降低了特征点提取所需的运算时间;同时通过分块处理以及OpenMP多线程技术实现了特征点匹配及仿射模型计算过程的CPU并行处理。实验表明:本方法相对于传统的SIFT算法平均加速3倍,并且对于固定大小的图像,本方法的特征点提取时间和特征点个数具有线性关系,加速比随着提取出特征点数量的增加而增大。

The method for remote sensing image registration based on scale-invariant feature transform（SIFT） has the advantage of hig haecuracy and good stability. However, the method is very time-consuming because of the large size of the image and the huge quantity of feature points. This paper presented a parallel acceleration method for very high res- olution remote sensing image registration which builds the Gaussian pyramid by hardware implementation on GPU. We used the shared memory to cache the temporary extremum at high speed when identifying the keypoint, which effectively decreases the time for the keypoint extraction. Meanwhile, we divided the whole image into blocks and used OpenMP to match the feature-points and build parallel acceleration of the affine model. Compared with the traditional registration method--SIFT, this method is 3 times faster. We concluded that the runtime of the keypoint extraction has linear re- lationship with the quantity of the keypoints, and the acceleration ratio raise with the density of the keypoints going up.