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摘要: 为了更好地解决航拍图像易受光照、旋转变化、尺度变化等影响，KAZE算法实时性较差以及基于K近邻的特征匹配算法耗时较长等问题，该文提出了一种基于改进KAZE的无人机航拍图像拼接算法.该方法首先利用加速的KAZE算法提取图像的特征点，采用二进制特征描述子FREAK（Fast retina keypoint）进行特征点描述，然后使用Grid-KNN算法进行特征点粗匹配，利用随机一致性算法对匹配的特征点进一步提纯并计算几何变换模型，最后采用加权平均算法对图像进行融合.实验结果表明，该文所提算法使图像在光照变化、旋转变化及尺度变化下具有较好的性能，且处理速度较KAZE算法与K近邻特征匹配算法有较大提升，是一种稳定、精确度高、拼接效果良好的无人机航拍图像拼接方法.
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关键词:
- 航拍图像拼接 /
- KAZE算法 /
- FREAK算法 /
- Grid-KNN算法
Abstract: The aerial image is subject to many effects including light, rotation changes, changes in dimensions and so on. The real-time performance of the KAZE algorithm is not desirable and the K-nearest neighbor (KNN) match algorithm takes a long time. Therefore, we propose a mosaic algorithm for UAV aerial image based on the improved KAZE. Firstly, we use an accelerated KAZE algorithm to extract feature points of the image, and use the binary feature descriptor fast retina keypoint (FREAK) to describe the feature points. Then, we adopt the Grid-KNN algorithm for rough match of these points, and use the random sample consensus algorithm for exact match and calculating the geometric transform model. Finally, we use the weighted average algorithm for image fusion. Experimental results show that compared with the KAZE algorithm and the KNN algorithm, the proposed algorithm has better performance on changes of illumination, rotation and scale, as well as processing speed. It is a stable, accurate and stitching algorithm.-
Key words:
- Aerial image mosaic /
- KAZE /
- FREAK /
- Grid-KNN
1) 本文责任编委 左旺孟 -
图 4 基于改进KAZE算法无人机航拍图像拼接流程
Fig. 4 The process of UAV aerial image mosaic based on improved KAZE algorithm
图 6 Leuven数据和Boat数据匹配正确率比较
Fig. 6 The comparison of correct matching rate for Leuven data and Boat data
图 7 Leuven数据和Boat数据匹配精度比较
Fig. 7 The comparison of matching accuracy for Leuven data and Boat data
表 3 匹配正确率比较
Table 3 The comparison of correct matching rate
图像编号 算法 匹配点对数 正确点对数 CMR (%) SIFT 1 492 1 359 91.15 图 5 (a) ORB 2 253 2 092 81.94 KAZE 1 427 1 436 99.93 本文算法 1 358 1356 99.85 SIFT 1 212 1 063 87.71 图 5 (b) ORB 1 340 995 74.25 KAZE 1 415 1 294 91.45 本文算法 1 280 1 159 90.55 SIFT 1 527 1 464 95.87 图 5 (c) ORB 1 573 1 130 71.84 KAZE 1 438 1 392 96.80 本文算法 1 285 1 222 95.12 
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