基于MeanShift算法的航空影像联合分割

许佳佳

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基于MeanShift算法的航空影像联合分割

成像技术与图像处理 | 更新时间：2020-08-12

- 基于MeanShift算法的航空影像联合分割
- Joint segmentation using MeanShift algorithm for high resolution aerial images
- 液晶与显示 2014年29卷第4期页码：586-591
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所中国科学院航空光学成像与测量重点实验室,吉林长春,130033
- 作者简介：
  
  [ "许佳佳（1986-），男，河南信阳人，硕士，研究实习员，主要从事航空影像处理方面的研究。E-mail：xujia_whu@163.com" ]
- 基金信息：
  
  吉林省重大科技攻关项目（No.11ZDGG001）();国家自然科学基金青年基金（No.60902067）()
- DOI：
  中图分类号： TP391
- 收稿日期：2014-03-12，
  
  修回日期：2014-03-13，
  
  网络出版日期：2014-05-06，
  
  纸质出版日期：2014-08-05
- 稿件说明：
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许佳佳. 基于MeanShift算法的航空影像联合分割[J]. 液晶与显示, 2014,29(4): 586-591

XU Jia-jia. Joint segmentation using MeanShift algorithm for high resolution aerial images[J]. , 2014,29(4): 586-591
许佳佳. 基于MeanShift算法的航空影像联合分割[J]. 液晶与显示, 2014,29(4): 586-591 DOI：

XU Jia-jia. Joint segmentation using MeanShift algorithm for high resolution aerial images[J]. , 2014,29(4): 586-591 DOI：

摘要

为了实现在包含部分变化信息的相同区域不同时相的高分辨率航空影像上获得准确一致的分割结果，提出了两时相高分辨率航空影像联合分割方法。首先，对两时相影像进行高斯平滑处理，减小地物的内部差异以避免过分割；然后将两时相影像进行波段组合，采用主成分分析法剔除冗余数据，取其第一主分量作为灰度分量；最后对原始影像进行纹理分析，获得两时相影像的纹理信息作为纹理分量并与灰度分量组合在一起进行MeanShift分割。实验对比结果表明，该方法能够有效利用数据，节省处理时间，获得了较好的分割结果。

Abstract

In order to achieve exact and consistent segmentation result using two-phase high resolution aerial images of the same area which contain some changed information

a joint segmentation method is proposed. First

the two-phase images are manipulated using Guassian smooth filter to suppress the object's internal diversity and avoid over-segmentation. Then principal component analysis is carried out on the combined image contained all bands of two-phase images to eliminate the redundant information. And the first principal component will be as gray component. Finally

texture is extracted from two-phase origin images as texture component

and meanshift algorithm will be implemented on the image combined gray component with two-phase texture components. Experimental results indicate that the proposed method can use the image information effectively and save much processing time. It can obtain better segmentation result.

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references

Fukunaga K,Hostetler L. The estimation of the gradient of a density function,with applications in pattern recognition[J]. IEEE Transactions on Information Theory,1975,21(1):32-40.[2] Yizong C. Mean shift,mode seeking,and clustering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17 (8):790-799.[3] Comaniciu D,Meer P. Mean shift:a robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24 (5):603-619.[4] Comaniciu D,Ramesh V,Meer P. Real-time tracking of non-rigid objects using mean shift. IEEE Conference on Computer Vision and Pattern Recognition,2000:142-149.[5] 张宇洋,刘满华,韩韬. 基于Mean Shift图像分割和支持向量机判决的候梯人数视觉检测系统[J]. 光学精密工程,2013,21(4):1079-1085. Zhang Y Y,Liu M H,Han T. Elevator-waiting people counting system based on mean shift segmentation and SVM classification [J]. Opt. Precision Eng.,2013,21 (4):1079-1085. (in Chinese)[6] Ran H,Bao-Gang H,Wei-Shi Z,et al. Robust principal component analysis based on maximum correntropy criterion [J]. IEEE Transactions on Image Processing,2011,20 (6):1485-1494.[7] 李海森,张艳宁,姚睿,等. 基于主成分分析的直线运动模糊参数估计[J]. 光学精密工程,2013,21(10):2656-2663. Li H S,Zhang Y N,Yao R,et al. Parameter estimation of linear motion blur based on principal component analysis[J]. Opt. Precision Eng.,2013,21(10):2656-2663. (in Chinese)[8] 魏旭. 基于主成分分析的特征融合及其应用. 成都:电子科技大学,2008. Wei X. Research on methods and applications of image feature fusion based on PCA.Chengdu:University of Electronic Science and Technology of China,2008. (in Chinese)[9] 韩延祥,张志胜,郝飞,等. 灰度序列图像中基于纹理特征的移动阴影检测[J]. 光学精密工程,2013,21(11):2931-2942. Han Y X,Zhang Z S,Hao F,et al. Shadow detection based on texture features in gray sequence images [J]. Opt. Precision Eng.,2013,21(11):2931-2942. (in Chinese)[10] 张来刚,魏仲慧,何昕,等. 面向低纹理图像的快速立体匹配[J]. 液晶与显示,2013,28(3):450-458. Zhang L G,Wei Z H,He X,et al. New stereo matching method based edge extration[J].Chinese Journal of Liquid Crystals and Displays,2013,28(3):450-458. (in Chinese)[11] Ojala T,Pietikainen M,Harwood D. A comparative study of texture measures with classification based on feature distributions[J]. Pattern Recognition,1996,29 (1):51-59.[12] Hu X Y,Tao C V,Prenzel B. Automatic segmentation of high-resolution satellite imagery by integrating texture,intensity,and color features[J]. Photogramm Eng. Rem. S,2005,71(12):1399-1406.[13] 吴笑天,鲁剑锋,贺柏根,等. 雾天降质图像的快速复原[J]. 中国光学,2013,6 (6):892-899. Wu X T,Lu J F,He B G,et al. Fast restoration of haze-degraded image [J].Chinese Optics,2013,6(6):892-899. (in Chinese)[14] 朱明,杨航,贺柏根,等. 联合梯度预测与导引滤波的图像运动模糊复原[J]. 中国光学,2013,6(6):850-855. Zhu M,Yang H,He B G,et al. Image motion blurring restoration of joint gradient prediction and guided filter[J]. Chinese Optics,2013,6(6):850-855. (in Chinese)[15] 闫辉,许廷发,吴青青,等. 多特征融合匹配的多目标跟踪[J]. 中国光学,2013,6 (2):163-170. Yan H,Xu T F,Wu Q Q,et al. Multi-object tracking based on muli-feature joint matching [J]. Chinese Optics,2013,6(2):163-170. (in Chinese)[16] 龚俊亮,何昕,魏仲慧,等. 基于贝叶斯估计的剪切波域局部自适应图像去噪[J]. 液晶与显示,2013,28 (5):799-804. Gong J L,He X,Wei Z H,et al. Local adaptive image denoising based on bayesian estimation in shearlet domain[J]. Chinese Journal of Liquid Crystals and Displays,2013,28(5):799-804. (in Chinese)

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