针对目前遥感图像拼接算法有效匹配率低、拼接融合误差大的问题,对遥感图像拼接算法的匹配及融合拼接过程等进行研究。利用引导滤波器预处理图像,在快速高效的加速稳健特征(SURF)算法和低误差双向互选择匹配策略的基础上,采用随机一致性算法对匹配特征点进行进一步提纯;使用单应性转换矩阵计算图像间的相对位置关系,在拼接与融合阶段,根据韦伯定律和植物生长函数修改图像融合算法;采用S型非线性融合策略完成图像融合拼接。仿真结果表明,本文方法的匹配正确率提升了约1.01%~3.42%,图像融合拼接结果图的平均梯度提升了约84.86%~146.26%,信息熵提升了约0.77%~2.22%。在实现高效、低误差遥感图像匹配的基础上,本文方法提升了拼接质量、效率以及融合算法的稳健性。

To solve the current issues of low effective matching rate and large stitching error of the remote sensing image mosaic algorithm, the matching and fusion/stitching processes of a remote sensing image mosaic algorithm are investigated herein. This algorithm utilizes a guided filter to preprocess the images. Based on the fast and efficient speed-up robust feature approach and the low-error bidirectional mutual selection matching strategy, the matching feature points are further purified by the random sample consensus algorithm; then, a homography transformation matrix is adopted to calculate the relative positional relationship between the images. In the fusion/stitching phase, the image fusion algorithm is modified according to Weber's law and the plant growth function. Finally, image fusion stitching is completed using the nonlinear S-type nonlinear fusion strategy. The simulations reveal improvements in the matching accuracy, average gradient of the image fusion/stitching resultant graph, and information entropy by approximately 1.01%-3.42%, 84.86%-146.26%, and 0.77%-2.22%, respectively. Thus, with respect to realizing high-efficiency and low-error remote sensing image matching, improved splicing quality and efficiency as well as high robustness of the fusion algorithm are achieved.