摘要
针对传统SIFT算法在无人机遥感图像拼接中存在的运算缓慢、误匹配较多且计算过于复杂,无法满足遥感图像处理的实时性要求等缺陷,以及由于采集到的图像之间存在曝光差异等情况,直接进行叠加拼接后极大可能会在边界处产生重影错位的情况,文章提出了一种改进的SURF算法与融合算法用于无人机的遥感图像拼接。首先,在特征检测阶段,将SURF算法与Harris角点检测算法2种算法相结合,快速得到图像的特征点与特征描述子;在特征匹配阶段分为粗匹配与精匹配2个步骤:通过KNN算法对待拼接图像间特征点的粗匹配,以及应用RANSAC算法去除误匹配点的精匹配;在图像融合阶段,采用了基于距离的加权平均算法进行图像融合;最后,实验表明:文章所提出的算法处理速度相比于传统SUFT算法提升了近5倍,相比于其他改进算法,匹配精度也有所提高,并且该算法能够有效提高图像拼接后的质量与效果,解决了拼接痕印明显、重影、错位等现象可能发生的问题。
Due to the traditional SIFT algorithm, there are many problems such as slow operation, mis-matching and computational complexity in UAV remote sensing image splicing, which can not meet the real-time requirements of remote sensing image processing, and there are exposure differences between the collected images. In this case, the direct superposition and splicing may cause ghosting misalignment at the boundary. In this paper, an improved SURF algorithm and fusion algorithm for remote sensing image mosaic of drones were proposed. Firstly, in the feature detection phase, the SURF algorithm and the Harris corner detection algorithm were combined to obtain the feature points and feature descriptors of the image quickly. In the feature matching stage, it was divided into two steps, whinch were rough matching and fine matching. The rough matching of the feature points between the stitched images and the fine matching of the mismatched points were eliminated by using the RANSAC algorithm. In the image fusion stage, a weighted average algorithm based on distance was used for image fusion. The final experiment showed that the processing speed of the proposed algorithm was improved by nearly 5 times compared with the traditional SUFT algorithm. Compared with other improved algorithms, the matching accuracy was also improved, and this algorithm could effectively improve the quality of the image mosaic. The effect solved the problem that the splicing marks, ghosts, dislocations and other phenomena might occur.
作者
么鸿原
王海鹏
焦莉
林雪原
YAO Hongyuan;WANG Haipeng;JIAO Li;LIN Xueyuan(Naval Aviation University, Yantai Shandong 264001, China;Yantai Zhifu Teacher Training School, Yantai Shandong 264001, China)
出处
《海军航空工程学院学报》
2018年第2期181-186,200,共7页
Journal of Naval Aeronautical and Astronautical University
基金
国家自然科学基金资助项目(61471383)
