人工智能
计算机视觉
束流调整
计算机科学
航空影像
稳健性(进化)
匹配(统计)
由运动产生的结构
模式识别(心理学)
摄影测量学
图像(数学)
运动估计
数学
基因
统计
生物化学
化学
作者
Yongxian Zhang,Guorui Ma,Jiao Wu
出处
期刊:Remote Sensing
[MDPI AG]
日期:2022-01-26
卷期号:14 (3): 588-588
被引量:6
摘要
Robustness of aerial-ground multi-source image matching is closely related to the quality of the ground reference image. To explore the influence of reference images on the performance of air-ground multi-source image matching, we focused on the impact of the control point projection accuracy and tie point accuracy on bundle adjustment results for generating digital orthophoto images by using the Structure from Motion algorithm and Monte Carlo analysis. Additionally, we developed a method to learn local deep features in natural environments based on fine-tuning the pre-trained ResNet50 model and used the method to match multi-scale, multi-seasonal, and multi-viewpoint air-ground multi-source images. The results show that the proposed method could yield a relatively even distribution of feature corresponding points under different conditions, seasons, viewpoints, illuminations. Compared with state-of-the-art hand-crafted computer vision and deep learning matching methods, the proposed method demonstrated more efficient and robust matching performance that could be applied to a variety of unmanned aerial vehicle self- and target-positioning applications in GPS-denied areas.
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