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国土资源遥感  2012, Vol. 24 Issue (2): 28-33    DOI: 10.6046/gtzyyg.2012.02.06
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于FAST和SURF的遥感图像自动配准方法
李慧1,2,3, 蔺启忠1,2, 刘庆杰1,2
1. 中国科学院对地观测与数字地球科学中心, 北京 100094;
2. 中国科学院数字地球科学重点实验室, 北京 100094;
3. 中国科学院研究生院, 北京 100049
An Automatic Registration Method of Remote Sensing Imagery Based on FAST Corner and SURF Descriptor
LI Hui1,2,3, LIN Qi-zhong1,2, LIU Qing-jie1,2
1. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
2. Key Laborary of Digital Earth Sciences, Chinese Academy of Sciences, Beijing 100094, China;
3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 提出了基于加速分割检测特征(features from accelerated segment test,FAST)和加速鲁棒性特征(speeded-up robust features,SURF)的遥感图像自动配准方法。首先对参考图像与待配准图像进行HSI变换和高斯金字塔建立; 然后检测并提取FAST角点,计算各角点的SURF描述子,用K-D树匹配搜索策略得到2幅图 像的匹配点对; 再使用最小二乘迭代法剔除错误匹配点并拟合几何变换系数; 最后执行几何变换,得到配准后的图像。将该方法分别与基于SURF自动配准方法和ENVI软件中自动获取配准点的方法进行对比实验,结果表明,利用该方法能够获得更多的匹配点对,具有更高的几何配准精度,但在尺度不变性方面略逊于SURF算法。
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关键词 地理空间信息农作物病虫害气象等级预报    
Abstract:An automatic geometry registration method based on Features from Accelerated Segment Test (FAST) corner detector and Speeded Up Robust Features (SURF) is proposed in this paper. Firstly, applying HSI transform on both the reference image and the image to registration, and then building gauss pyramid of the images. Secondly, detecting and extracting the FAST corner points of both images, and calculating the SURF descriptors of the corner points, following by searching match point pairs by K-D tree. Thirdly, iteratively using partial minimum least squares to remove error point pairs and then calculate the geometry transform coefficients. Lastly, excuting the geometry transform to get the registration image. An experiment on two groups of images was performed, in which the proposed method was respectively compared with the automatic registration method based on SURF features and the method used in ENVI software to obtain ground control points automatically, and the results show that the proposed method can get more match points and obtain higher geometric accuracy, except which is slightly inferior to SURF algorithm in scale invariance.
Key wordsgeospatial information    crop diseases and pests    meteorology level forecast
收稿日期: 2011-09-13      出版日期: 2012-06-03
: 

TP 751.1

 
基金资助:

国家"十一五"科技支撑重点项目(编号: 2006BAB01A02)、中国科学院对地观测与数字地球科学中心数字地球科学平台重大项目(编号: DESP01-04-10)和国家自然科学基金项目(编号: 41001266)共同资助。

引用本文:   
李慧, 蔺启忠, 刘庆杰. 基于FAST和SURF的遥感图像自动配准方法[J]. 国土资源遥感, 2012, 24(2): 28-33.
LI Hui, LIN Qi-zhong, LIU Qing-jie. An Automatic Registration Method of Remote Sensing Imagery Based on FAST Corner and SURF Descriptor. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 28-33.
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https://www.gtzyyg.com/CN/10.6046/gtzyyg.2012.02.06      或      https://www.gtzyyg.com/CN/Y2012/V24/I2/28
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[1] 李轩, 郭安红, 庄立伟. 基于GIS的主要农作物病虫害气象等级预报系统研究[J]. 国土资源遥感, 2012, 24(1): 104-109.
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