基于小面元配准的倾斜遥感图像拼接

针对倾斜遥感图像拼接中存在配准精度不高,重叠区域出现重影的问题,提出了利用三角网进行小面元配准及加权融合的拼接方法。首先求解全局单应性矩阵进行图像预对齐并利用转换矩阵提取图像重叠区域,然后利用特征匹配对构建重叠区域Delaunay三角网并对相应三角网逐个进行仿射变换实现精确配准,最后利用渐出渐入式融合消除图像重叠区域重影,提高了图像拼接目视效果。通过主观评价(CCIR500-1的主观评价标准)与客观评价(PSNR评价和SSIM评价)相结合的方式对图像配准及拼接质量进行评价。实验结果表明：该方法能够有效提高图像配准精度,消除图像拼接重叠区域的重影,相比于当前主要算法,该算法配准精度更高,图像拼接目视效果更好,能有效地应用到倾斜图像拼接领域。

Inclined remote sensing image mosaic based on small facet correction

Aiming at the problem of low registration accuracy in the mosaic of oblique remote sensing images and ghosting in overlapping areas, a mosaic method using triangulation for small bin registration and weighted fusion is proposed. First, solve the global homography matrix for image pre-alignment and use the transformation matrix to extract the image overlap area, then use feature matching to construct the overlap area Delaunay triangulation and perform affine transformation on the corresponding triangulation one by one to achieve precise registration. Finally, the fade - in and fade - out fusion is used to eliminate the shadow of the overlapping area and improve the visual effect of image mosaic. The quality of image registration and mosaic was evaluated by combining subjective evaluation (CCIR500-1 subjective evaluation standard) with objective evaluation (PSNR evaluation and SSIM evaluation). Experimental results show that this method can effectively improve the registration accuracy and eliminate the shadow of the overlapping area of image mosaic. Compared with the current main algorithms, this algorithm has higher registration accuracy and better visual effect of image mosaic. Therefore, it can be effectively applied to the field of oblique image mosaic.