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论文:2016,Vol:34,Issue(2):328-332 |
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引用本文： |
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马旭, 程咏梅, 郝帅. 尺度变化下飞机着降区自适应分割算法[J]. 西北工业大学学报 |
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Ma Xu, Cheng Yongmei, Hao Shuai. Adaptive Segmentation Algorithm of Landing Zone under Change of Scale[J]. Northwestern polytechnical university |
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| 尺度变化下飞机着降区自适应分割算法 |
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| 马旭1,2, 程咏梅1, 郝帅2 |
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1. 西北工业大学自动化学院, 陕西西安 710072;
2. 西安科技大学 电气与控制学院, 陕西 西安 710054 |
| 摘要: |
| 无人机利用视觉在未知区域寻找着降区时,获取的航拍图像易受光照影响并存在尺度变化,对航拍图像进行分割的主要目的是找出满足无人机着降区域大小且地物类型(如土地、湖泊等)相同的区域,所以要求其分割算法既要有聚类的能力,又具有将不同类的地物进行有效分割的能力。针对上述航拍图像的特点和分割任务需求,提出了一种尺度变化下飞机着降区自适应分割算法。首先根据无人机当前的高度信息和对着降区几何大小的要求,计算当前时刻图像的地面分辨率及满足无人机着陆时所需的最小像素数;然后提出一种自适应mean shift分割算法对航拍图像进行粗分割,其中核函数的带宽参数根据计算出的最小像素数结合最大类间方差法的阈值进行选取;接着对粗分割的图像利用Canny算子进行边缘提取实现图像的精细分割,得到最终分割结果;最后利用Google Earth在不同场景和尺度下的图像进行分割实验,实验结果表明该算法可以满足分割的任务需求实现准确分割,并对光照和尺度变化较为鲁棒。 |
| 关键词:
航拍图像
未知区域
多特征融合
图像分割
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| Adaptive Segmentation Algorithm of Landing Zone under Change of Scale |
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| Ma Xu1,2, Cheng Yongmei1, Hao Shuai2 |
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1. Department of Automatic Control, Northwestern Polytechnical University, Xi'an 710072, China;
2. School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China |
| Abstract: |
| The acquired aerial image is susceptible to illumination and scale changes when UAV is searching for a suitable landing zone based on vision. The main purpose of the aerial image segmentation is to identify the landing zone which is roomy enough for UAV to land and has the same object types(such as land, lakes, etc). Therefore, the segmentation algorithm not only has the capability of clustering but also has the capability of segmenting the different type objects. According to the characteristics of the aerial image and the needs of the segmentation task, an adaptive segmentation algorithm of landing zone under the change of scale is proposed. First, the needed minimum pixel is calculated for UAV landing according to the current height of UAV, the size of the landing area and the ground resolution of the image. Second, the Mean Shift algorithm is employed for image coarse segmentation, and the bandwidth of the kernel function in Mean Shift is calculated according to the minimum pixel gotten in previous step in combination with the threshold in maximum between-cluster variance. Third, the edge of the coarse segmentation image is drawn by using the Canny operator and the final segmentation result is gotten. Finally, the aerial images at different scenarios and scales preliminarily that selected with Google Earth are employed in segmentation experiments. Experimental results demonstrates that the proposed algorithm can meet the mission requirements for accurate segmentation and it is robust to illumination and scale change. |
| Key words:
algorithms
bandwidth
caculations
data fusion
edge detection
experiments
flowcharting
image acquisition
image segmentation
iterative methods
lighting
mathematical operators
MATLAB
robustness
control systems
scalability
unmanned aerial vehicles(UVA)
aerial image
Google Earth
mean shift image segmentation
multi-feature fusion
unfamiliar area
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| 收稿日期: 2015-10-20
修回日期:
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| DOI: |
| 基金项目: 西安科技大学培育基金(2014016)资助 |
| 通讯作者:
Email： |
| 作者简介: 马旭(1985-),女,西北工业大学博士研究生,主要从事图像处理与视觉导航的研究。
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| 作者相关文章 |
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| 马旭 在本刊中的所有文章 |
| 程咏梅 在本刊中的所有文章 |
| 郝帅 在本刊中的所有文章 |
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参考文献: |
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[1] Scherer S, Chamberlain L, Singh S. Autonomous Landing at Unprepared Sites by a Full-Sale Helicopter[J]. Robotics and Autonomous Systems, 2012, 60(12):1545-1562
[2] Mejias L, Fitzgerald D L, Eng P C, et al. Forced Landing Technologies for Unmanned Aerial Vehicles:Towards Safer Operations[J]. Aerial Vehicles, 2009(1):415-442
[3] Lu A, Ding W, Li H. Multi-information Based Safe Area Step Selection Algorithm for UAV's Emergency Forced Landing[J]. Journal of Software, 2013, 8(4):995-1002
[4] Michel J, Youssefi D, Grizonnet M. Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images[J]. IEEE Trans on Geoscience and Remote Sensing, 2015, 53(2):952-964
[5] 朱俊杰, 杜小平, 范湘涛, 等. 融合边缘特征与区域特征的高分辨率遥感图像分割方法[J]. 高技术通讯, 2012, 22(9):937-943 Zhu Junjie, Du Xiaoping, Fan Xiangtao, et al. Segmentation of High-Resolution Remote Sensing Images Based on Fusing Edge and Region Features[J]. High Technology Letters, 2012, 22(9):937-943(in Chinese)
[6] Sumengen B, Manjunath B S. Multi-Scale Edge Detection and Image Segmentation[C]//European Signal Processing Conference(EUSIPCO), 2005
[7] Abo-Zahhad M, Gharieb R R, Ahmed S M, et al. Edge Detection with a Preprocessing Approach[J]. Journal of Signal & Information Processing, 2014, 5(4):123-134 |
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