A fast feature extraction and matching algorithm for unmanned aerial vehicle images

被引：0
|
作者：
Yu H. [1 ]
Yang W. [1 ]
机构：
[1] School of Electronic Information, Wuhan University, Wuhan
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2016年 / 38卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Feature extraction; Feature matching; Image stitching; Unmanned Aerial Vehicle (UAV);
D O I：
10.11999/JEIT150676
中图分类号：
学科分类号：
摘要：
Unmanned Aerial Vehicle (UAV) images are characterized by a very high spatial resolution, and consequently by more abundant information of the edge and the texture. The conventional stitching methods, which use Speeded Up Robust Features (SURF) and kd-tree based nearest neighbor matching, are facing with new challenges for processing UAV images. In this paper, a fast feature extraction and matching algorithm is proposed for more efficient stitching of UAV images. Firstly, the Local Difference Binary (LDB) algorithm is used to describe the feature, which could reduce the dimension of feature without sacrificing its discrimination. Then, the Local Sensitive Hash (LSH) is used to replace kd-tree search structure, which achieves nearest neighbor matching more efficiently. Compared with the conventional stitching method, experimental results demonstrate that the proposed method achieves a higher accuracy and greater efficiency, which is more applicable to rapid mapping of UAV images. © 2016, Science Press. All right reserved.
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收藏
页码：509 / 516
页数：7
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