结合Harris和改进SIFT算法的遥感图像配准方法

王亚丽

doi:10.13568/j.cnki.651094.651316.2020.12.22.0002

您当前的位置：

首页 >

文章列表页 >

结合Harris和改进SIFT算法的遥感图像配准方法

信息科学与技术 | 更新时间：2026-01-21

- 结合Harris和改进SIFT算法的遥感图像配准方法
- 结合Harris和改进SIFT算法的遥感图像配准方法
- 新疆大学学报（自然科学版中英文） 2021年38卷第6期页码：699-704
- 作者机构：
  
  喀什大学计算机科学与技术学院
- 作者简介：
- 基金信息：
  
  喀什大学校内一般课题（2020）2738
- DOI：10.13568/j.cnki.651094.651316.2020.12.22.0002
  中图分类号： TP751
- 纸质出版：2021
- 稿件说明：
移动端阅览
[1]王亚丽.结合Harris和改进SIFT算法的遥感图像配准方法[J].新疆大学学报(自然科学版)(中英文),2021,38(06):699-704.
[1]王亚丽.结合Harris和改进SIFT算法的遥感图像配准方法[J].新疆大学学报(自然科学版)(中英文),2021,38(06):699-704. DOI： 10.13568/j.cnki.651094.651316.2020.12.22.0002.

DOI：10.13568/j.cnki.651094.651316.2020.12.22.0002.

摘要

针对不同传感器或者不同波带的遥感图像配准中存在灰度差异性较大的问题

提出一种遥感图像配准算法.该算法通过Sobel算子与原图像卷积求取遥感图像间的一阶梯度图像来降低图像之间灰度差异

在梯度幅值图像上构建高斯尺度空间

在高斯尺度空间图像上检测Harris角点.用最近邻与次近邻比值法实现图像粗匹配

利用随机抽样一致性算法消除误匹配

并求得仿射变换模型参数

完成遥感图像配准.实验结果表明:该算法能克服遥感图像之间灰度差异问题

鲁棒性增强

与其它几种经典算法对比

对有较大灰度差异的遥感图像来说

可以成功实现配准

且配准精度较高.

Abstract

Aiming at the problem of large gray difference in remote sensing image registration of different sensors or different wavebands

a remote sensing image registration algorithm is proposed. This algorithm reduces the problem of grayscale differences between images by convolving the Sobel operator with the original image to obtain a gradient image between remote sensing images

and constructing Gaussian scale space on the gradient amplitude image

and detecting Harris corner on the Gaussian scale space image. The method of the ratio of the nearest neighbor to the next nearest neighbor is used to realize the rough image matching

the random sampling consensus algorithm is used to eliminate the mismatches

and the parameter of affine transformation model is obtained to register remote sensing images. The experimental results show that the algorithm overcomes the problem of grayscale differences between remote sensing images and has enhanced robustness. Compared with several other classic algorithms

it can successfully achieve registration for remote sensing images with large gray-scale differences

and the matching precision is higher.

关键词

Keywords

references

吴一全,谢芬.基于对比度Harris的快速鲁棒图像配准算法[J].北京理工大学学报, 2020, 40(3):316-324.WU Y Q, XIE F. A fast and robust image registration algorithm based on contrast Harris[J]. Journal of Beijing Institute of Technology, 2020, 40(3):316-324.(in Chinese)

WANG Y L, LAI H C, MA H B, et al. A novel Harris feature detection-based registration for remote sensing image[J]. Journalof the Indian Society of Remote Sensing, 2020, 48(9):1245-1252.

余银峰,祝美玲.一种新的无监督SAR影像变化检测[J].新疆大学学报(自然科学版), 2019, 36(4):442-447.YU Y F, ZHU M L. A novel unsupervised sar image change detection[J]. Journal of Xinjiang University(Natural Science Edition),2019, 36(4):442-447.(in Chinese)

朱平哲.基于DCT与PSO的可见光与红外图像融合方法[J].新疆大学学报(自然科学版), 2018, 35(4):452-458.ZHU P Z. Method for visible and infrared image fusion using discrete cosine transform and particle swarm optimization[J]. Journal of Xinjiang University(Natural Science Edition), 2018, 35(4):452-458.(in Chinese)

保文星,桑斯尔,沈象飞.基于信息熵约束和KAZE特征提取的遥感图像配准算法研究[J].光学精密工程, 2020, 28(8):1810-1819.BAO W X, SANG S E, SHEN X F. Research on remote sensing image registration algorithm based on information entropy constraint and KAZE feature extraction[J]. Optical Precision Engineering,2020, 28(8):1810-1819.(in Chinese)

HARRIS C G, STEPHENS M J. A combined corner and edge detector[C]. Alvey:Alvey Vision Conference, 1988, 1550.

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.

姚国标,邓喀中,张力,等.基于Harris-Affine的宽基线立体影像LSM匹配方法[J].中南大学学报(自然科学版), 2014, 45(8):2661-2668.YAO G B, DENG K Z, ZHANG L, et al. A wide-baseline stereo image LSM matching method based on Harris-Affine[J]. Journal of Central South University(Natural Science Edition), 2014, 45(8):2661-2668.(in Chinese)

MA D, LAI H C. Remote sensing image matching based improved ORB in NSCT domain[J]. Journal of the Indian Society of Remote Sensing, 2019, 47(5):801-807.

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

BAY H, TUYTELAARS T, GOOL L. SURF:speeded up robust features[C]. Berlin:Proceedings of the 9th European conference on Computer Vision-Volume Part I, Springer-Verlag, 2006:404-417.

黄源,张福泉.基于差分制约耦合三角网约束的图像匹配算法[J].新疆大学学报(自然科学版), 2018, 35(4):437-444.HUANG Y, ZHANG F Q. An image matching algorithm based on differential constraint model coupling triangulation constraint[J].Journal of Xinjiang University(Natural Science Edition), 2018, 35(4):437-444.(in Chinese)

岳娟,高思莉,李范鸣,等.具有近似仿射尺度不变特征的快速图像匹配[J].光学精密工程, 2020, 28(10):2349-2359.YUE J,GAO S L,LI F M, et al. Fast image matching algorithm with approximate affine and scale invariance[J]. Optics and Precision Engineering, 2020, 28(10):2349-2359.(in Chinese)

LI Q, WANG G, LIU J, et al. Robust scale-invariant feature matching for remote sensing image registration[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2):287-291.

FAN D K, YE Y X, PAN L, et al. A remote sensing adapted image registration method based on SIFT and phase congruency[J].International Conference on Image Analysis and Signal Processing, IEEE, 2011:326-331.

DELLINGER F, DELON J, GOUSSEAU Y, et al. SAR-SIFT:a SIFT-like algorithm for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1):453-466.

冷成财,张海鹏,张聪炫,等.基于尺度、方向和距离约束的改进SIFT配准方法[J].纳米技术与精密工程, 2017, 15(1):36-43.LENG C C, ZHANG H P, ZHANG C X, et al. Improved SIFT registration method based on scale, orientation and distance constraints[J]. Nanotechnology and precision engineering, 2017, 15(1):36-43.(in Chinese)

FISCHLER M A, BOLLES R C. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6):381-395.

WANG W X. Remote sensing image automatic registration on multi-scale Harris-Laplacian[J]. Journal of the Indian Society of Remote Sensing, 2015, 43(3):501-511.

浏览量

971

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

SW-SRD：新疆沙湾地区遥感图像超空间分辨率重建数据集