一种改进的SURF彩色遥感图像配准算法

张二磊; 马骏; 王晓田

doi:10.3788/YJYXS20173202.0144

您当前的位置：

首页 >

文章列表页 >

一种改进的SURF彩色遥感图像配准算法

显示与图像 | 更新时间：2020-07-07

- 一种改进的SURF彩色遥感图像配准算法
- Improved SURF algorithm for color remote sensing image registration
- 液晶与显示 2017年32卷第2期页码：144-152
- 作者机构：
  
  1.中国空空导弹研究院第六研究所, 河南洛阳 471009
  2.中国兵器工业第二O三研究所, 陕西西安 710065
  3.西北工业大学航天学院, 陕西西安 710072
- 作者简介：
  
  [ "张二磊(1981-), 男, 河南西平人, 助理工程师, 2009年于西安电子科技大学获得硕士学位, 主要从事红外场景仿真方面的研究。E-mail:zwjllhtt@scut.edu.cn" ]
  王晓田(1989-), 男, 河北石家庄人, 博士研究生, 2013年于西北工业大学电子信息学院获得硕士学位, 2016年就读于西北工业大学航天学院, 主要红外遥感算法研究。E-mail:18710993786@163.com
- 基金信息：
  
  航天科技创新基金(CASC201105);Aerospace Science and Technology Innovation Fund(CASC201105)
- DOI：10.3788/YJYXS20173202.0144
  中图分类号： TP751.1
- 收稿日期：2016-08-24，
  
  录用日期：2016-10-21，
  
  纸质出版日期：2017-02-05
- 稿件说明：
移动端阅览
张二磊, 马骏, 王晓田. 一种改进的SURF彩色遥感图像配准算法[J]. 液晶与显示, 2017,32(2):144-152.

Er-lei ZHANG, jun MA, Xiao-tian WANG. Improved SURF algorithm for color remote sensing image registration[J]. Chinese journal of liquid crystals and displays, 2017, 32(2): 144-152.
张二磊, 马骏, 王晓田. 一种改进的SURF彩色遥感图像配准算法[J]. 液晶与显示, 2017,32(2):144-152. DOI： 10.3788/YJYXS20173202.0144.

Er-lei ZHANG, jun MA, Xiao-tian WANG. Improved SURF algorithm for color remote sensing image registration[J]. Chinese journal of liquid crystals and displays, 2017, 32(2): 144-152. DOI： 10.3788/YJYXS20173202.0144.

摘要

为了进一步提高彩色遥感图像的配准精度，针对遥感图像配准过程中色彩信息利用率低的问题，提出一种改进的SURF（Speeded Up Robust Feature）彩色遥感图像配准算法。该算法首先在对彩色遥感图像进行特征点检测基础上，对特征点描述算子进行改进，以使颜色空间变换后得到的特征点色彩信息添加到原描述算子中；其次，对特征点描述算子进行归一化处理，以增加算子的独特性和对旋转、尺度、光照的鲁棒性；最后，利用Hession矩阵的迹提高精度以及欧氏距离比率完成特征点匹配，得到正确匹配点对，再通过变换矩阵参数估计和插值处理得到配准图像。实验结果表明，在保证实时性的条件下，该算法相比经典SIFT算法和SURF算法，准确性和稳定性都有一定提高，具有一定的理论和应用价值。

Abstract

To further improve the registration precision of color remote sensing image

an improved SURF registration algorithm is put forward aiming at solving the problem of low utilization rate of color information. Firstly

based on the detection of feature points

a modified descriptor was constructed adding in color information got with color space transformation; then the normalized processing for the modified descriptor was done to increase its uniqueness and robustness to rotation

scale and illumination; finally

by trace of Hession matrix to improve precision

the correct matching points could be acquired through the Euclidean distance ratio. And the registered image was obtained based on parameter estimation and interpolation processing. Experimental results show

compared with the original SURF algorithm

that the improved algorithm has certain improvement in terms of accuracy and stability while guaranteeing the real time. It has certain values in both theory and practice.

关键词

Keywords

references

温红艳.遥感图像拼接算法研究[D].武汉:华中科技大学, 2009:17-35.

WEN H Y. Research on algorithm of remote sensing image mosaics[D]. Wuhan:Huazhong University of Science and Technology, 2009:17-35. (in Chinese)

余先川, 吕中华, 胡丹.遥感图像配准技术综述[J].光学精密工程, 2013, 21(11):2960-2972.

YU X C, LÜ Z H, HU D. Review of remote sensing image registration techniques[J]. Optics and Precision Engineering , 2013, 21(11):2960-2972. (in Chinese)

THANGAVEL P, KOKILA R. An extension of FFT based image registration [M]//MEGHANATHAN M, NAGAMALAI D, CHAKI N. Advances in Computing and Information Technology. Berlin, Heidelberg:Springer, 2013, 177:729-737.

HARRIS C, STEPHENS M. A combined corner and edge detector[C]// Proceedings of the 4th Alvey Vision Conference. Manchester:University of Manchester, 1988:147-152.

SMITH S M, BRADY J M. SUSAN-a new approach to low level image processing[J]. International Journal of Computer Vision , 1997, 23(1):45-78.

LINDEBERG T. Feature detection with automatic scale selection[J]. International Journal of Computer Vision , 1998, 30(2):79-116.

LOWE D G. Object recognition from local scale-invariant features[C]// Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra:IEEE, 1999, 2:1150-1157.

BAY H, ESSA A, TUYTELAARS T, et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding , 2008, 110(3):346-359.

SIMARD P Y, BOTTOU L, HAFFNER P, et al. Boxlets:a fast convolution algorithm for signal processing and neural networks[C]// Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems Ⅱ. Cambridge, MA, USA:MIT Press, 1999:571-577.

VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]// Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA:IEEE, 2001, 1:I-511-I-518.

LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110.

Fan P, Men A D, CHEN M Y, et al. Color-surf:a surf descriptor with local kernel color histograms[C]// Proceedings of IEEE International Conference on Network Infrastructure and Digital Content , Beijing, China:2009:726-730.

林晓帆, 林立文, 邓涛.基于SURF描述子的遥感影像配准[J].计算机工程, 2010, 36(12):216-218.

LIN X F, LIN L W, DENG T. Remote sensing image registration based on SURF descriptor[J]. Computer Engineering , 2010, 36(12):216-218. (in Chinese)

程国华, 王阿川, 陈舒畅, 等.多源遥感影像高精度自动配准方法研究[J].液晶与显示, 2016, 31(6):604-612.

CHENG G H, WANG A C, CHEN S C, et al. High accuracy-automatic registration method research on multi-source remote sensing image[J]. Chinese Journal of Liquid Crystals and Displays , 2016, 31(6):604-612. (in Chinese)

许佳佳.结合Harris与SIFT算子的图像快速配准算法[J].中国光学, 2015, 8(4):574-581.

XU J J. Fast image registration method based on Harris and SIFT algorithm[J]. Chinese Optics, 2015, 8(4):574-581. (in Chinese)

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于SURF和SIFT特征的视频镜头分割算法

基于SIFT-SVM的嵌入式印鉴识别系统设计

结合尺度空间FAST角点检测器和SURF描绘器的图像特征

基于SURF的抗视角变换图像匹配算法

结合区域分割的SIFT图像匹配方法