Image registration is a common task in remote sensing, consisting in aligning different images of the same scene. In the particular case of hyperspectral images, the exploitation not only of the spatial information contained in the image but also of the spectral information helps to improve the registration. An example of registration method exploiting all the information contained in the images is HSI–KAZE, which is based on feature detection and detects keypoints using nonlinear diffusion filtering. The algorithm is oriented toward extreme situations in which the images are very different in terms of scale, rotation and displacement. In this paper, an efficient implementation of the HSI–KAZE algorithm on GPU using CUDA is proposed. A detailed analysis of the implementation as well as a performance comparison to an OpenMP multicore implementation is also presented. The resulting algorithm is suitable for on-board processing of high-resolution images.

中文翻译：

---

使用 KAZE 特征的高光谱图像的 GPU 加速配准

图像配准是遥感中的一项常见任务，包括对齐同一场景的不同图像。在高光谱图像的特殊情况下，不仅利用图像中包含的空间信息而且利用光谱信息有助于改进配准。利用图像中包含的所有信息的配准方法的一个例子是 HSI-KAZE，它基于特征检测并使用非线性扩散过滤检测关键点。该算法面向极端情况，其中图像在比例、旋转和位移方面非常不同。在本文中，提出了一种使用 CUDA 在 GPU 上有效实现 HSI-KAZE 算法的方法。还提供了对实现的详细分析以及与 OpenMP 多核实现的性能比较。所得算法适用于高分辨率图像的机载处理。