基于改进SIFT的多光谱图像匹配算法

doi:10.11896/j.issn.1002-137X.2019.04.044

计算机科学 ›› 2019, Vol. 46 ›› Issue (4): 280-284.doi: 10.11896/j.issn.1002-137X.2019.04.044

基于改进SIFT的多光谱图像匹配算法

孙雪强^1,2, 黄旻¹, 张桂峰¹, 赵宝玮¹, 丛麟骁^1,2

中国科学院光电研究院计算光学成像技术重点实验室北京100094¹
中国科学院大学材料科学与光电技术学院北京100049²

收稿日期:2018-03-17 出版日期:2019-04-15 发布日期:2019-04-23
通讯作者: 黄旻 (1976-),男,博士,研究员,主要研究方向为计算光学成像,E-mail:huangmin@aoe.ac.cn(通信作者)
作者简介:孙雪强(1992-),男,硕士,主要研究方向为多光谱图像数据处理,E-mail:1695005670@qq.com;张桂峰(1979-),男,博士,副研究员,主要研究方向为遥感影像处理;赵宝玮(1983-),男,博士,主要研究方向为遥感信息处理;丛麟骁(1988-),男,博士,主要研究方向为偏振型干涉光谱仪。
基金资助:
本文受国家自然科学基金(61405203,61405204),中国科学院光电研究院创新项目(Y70B02A11Y)资助。

Multispectral Image Matching Algorithm Based on Improved SIFT

SUN Xue-qiang^1,2, HUANG Min¹, ZHANG Gui-feng¹, ZHAO Bao-wei¹, CONG Lin-xiao^1,2

Key Laboratory of Computation Optical Imaging Technology,Academy of Opto-Electronics,Chinese Academy of Sciences,Beijing 100094,China¹
College of Materials Science and Optoelectronic Technology,University of Chinese Academy of Sciences,Beijing 100049,China²

Received:2018-03-17 Online:2019-04-15 Published:2019-04-23

摘要/Abstract

摘要： 针对多光谱图像在各谱段匹配时需要兼顾速度与精度的问题,文中从以下几个方面对SIFT算法进行了改进。针对SIFT算法中特征描述子的维数过高而导致的匹配速度过慢、匹配率低等问题,通过改进特征描述子的结构来实现对描述子的降维。在SIFT特征匹配方面,根据Hessian矩阵的迹的正负确定特征点是极大值点还是极小值点,为后续特征向量匹配缩小搜索范围;然后根据特征点的位置信息剔除部分匹配点对。实验结果表明,改进算法不仅保留了SIFT算法对旋转和亮度等不变性的优势,而且能够有效减少运行时间,并在一定程度上提高了匹配率。

关键词: SIFT, 多光谱图像, 特征描述子, 特征向量匹配

Abstract: In order to solve the problem that the speed and accuracy need to be taken into account simultaneously when conducting multispectral image matching,this paper improved the SIFT algorithm from the following several aspects.Aiming at the problems such as the slow matching speed and low matching rate caused by high dimension of feature descriptors,this paper improved the structure of feature descriptors to reduce the dimensions of descriptors.In the aspect of SIFT feature matching,firstly,the feature point is determined as the maximum point or minimum point according to the trace of Hessian matrix,which can narrow subsequent search range for the feature vector matching.Then,the partial matching point pairs are eliminated based on the position information of feature points.The experimental results show that the improved algorithm not only preserves the invariance advantages of the traditional algorithm,such as rotation and brightness,but also can effectively reduce the running time,and improve the matching rate on a certain extent.

Key words: Feature descriptor, Feature vector matching, Multispectral image, SIFT

中图分类号:

TP391

孙雪强, 黄旻, 张桂峰, 赵宝玮, 丛麟骁. 基于改进SIFT的多光谱图像匹配算法[J]. 计算机科学, 2019, 46(4): 280-284. https://doi.org/10.11896/j.issn.1002-137X.2019.04.044

SUN Xue-qiang, HUANG Min, ZHANG Gui-feng, ZHAO Bao-wei, CONG Lin-xiao. Multispectral Image Matching Algorithm Based on Improved SIFT[J]. Computer Science, 2019, 46(4): 280-284. https://doi.org/10.11896/j.issn.1002-137X.2019.04.044

参考文献

[1]LOWE D G.Object recognition from localscale-invariant fea- tures[C]∥ Proceedings of the Seventh IEEE International Conference on Computer Vision.1999:1150-1157.
[2]LOWE D G.Distinctive image features from scale invariant keypoints [J].International Journal of Computer Vision,2004,60(2):91-110.
[3]SUZUKI T,AMANO Y,HASHIZUME T.Vision based localization of a small UAV for generating a large mosaic image [C]∥IEEE Sice Annual Conference.2010:2960-2964.
[4]KE S,WANG B L,HUANG X Y.An improved SIFT algorithm and its application in medical image registration [J].Journal of Xiamen University:Nature Science,2010,49(3):354-358.(in Chinese) 柯杉,王博亮,黄晓阳.一种改进的SIFT算法及其在医学图像配准中的应用[J].厦门大学学报:自然科学版,2010,49 (3):354-358.
[5]XIONG Y,MA H M.Extraction and Application of 3D Object SIFT Feature [J].Journal of Image and Graphics,2010,15 (5):814-819.(in Chinese) 熊英,马惠敏.3维物体SIFT特征的提取与应用[J].中国图象图形学报,2010,15(5):814-819.
[6]KOUNALAKIS T,TRIANTAFYLLIDIS G A.3D scene’s object detection and recognition using depth layers and SIFT-based machine learning [J].3d Research,2011,2(3):1-11.
[7]BAI J,MA Y,LI J,et al.Novel averaging window filter for SIFT in infrared face recognition [J].Chinese Optics Letters,2011,9(8):081002.
[8]BAY H,TUYTELAARS T,GOOL L V.SURF:Speeded Up Robust Features[J].Computer Vision & Image Understanding,2006,110(3):404-417.
[9]YAN K,SUKTHANKAR R.PCA-SIFT:a more distinctive representation for local image descriptors[C]∥IEEE Computer Society Conference on Computer Vision & Pattern Recognition.2004:506-513.
[10]LUO J,GWUN O.A Comparison of SIFT,PCA-SIFT and SURF [J].International Journal of Image Processing,2009,3(4):143-152.
[11]MIKOLAJCZYK K,SCHMID C.A performance evaluation of local descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[12]ZHAO Y,JIANG J G,WANG R C.An Optimized SIFT Matching Based on RANSAC [J].Opto-Electronic Engineering,2014,41(8):58-65.(in Chinese) 赵烨,蒋建国,洪日昌.基于 RANSAC 的 SIFT 匹配优化[J].光电工程,2014,41(8):58-65.
[13]ZENG L,GU D L.A SIFT feature descriptor based on sector area partitioning[J].Acta Automatica Sinica,2012,38(9):1513-1519.(in Chinese) 曾峦,顾大龙.一种基于扇形区域分割的SIFT特征描述符[J].自动化学报,2012,38(9):1513-1519.
[14]ZOU C M,XU Z Q,XUE D.SIFT algorithm based on block matching[J].Computer Science,2015,42(4):311-315.(in Chinese) 邹承明,徐泽前,薛栋.一种基于分块匹配的SIFT算法[J].计算机科学,2015,42(4):311-315.
[15]LIU J,FU W P,WANG W.Image matching based on improved SIFT algorithm[J].Chinese Journal of Scientific Instrument,2013,34(5):1107-1112.(in Chinese) 刘佳,傅卫平,王雯.基于改进SIFT算法的图像匹配[J].仪器仪表学报,2013,34(5):1107-1112.

相关文章 15

[1]	胡育诚, 芮挺, 杨成松, 王东, 刘恂. 基于改进SIFT的无人机航拍图像快速配准研究 Study on Aerial Image Fast Registration from UAV 计算机科学, 2021, 48(8): 134-138. https://doi.org/10.11896/jsjkx.200600140
[2]	高玉潼, 雷为民, 原玥. 复杂环境下基于聚类分析的人脸目标识别 Face Recognition Based on Cluster Analysis in Complex Environment 计算机科学, 2020, 47(7): 111-117. https://doi.org/10.11896/jsjkx.190500004
[3]	焦扬, 杨传颖, 石宝. 基于SVM相关反馈的鞋印图像检索算法 Relevance Feedback Method Based on SVM in Shoeprint Images Retrieval 计算机科学, 2020, 47(11A): 244-247. https://doi.org/10.11896/jsjkx.200400032
[4]	杨思燕,贺国旗,刘如意. 基于SIFT算法的大场景视频拼接算法及优化 Video Stitching Algorithm Based on SIFT and Its Optimization 计算机科学, 2019, 46(7): 286-291. https://doi.org/10.11896/j.issn.1002-137X.2019.07.044
[5]	邢文博, 杜志淳. 数字图像复制粘贴篡改取证 Digital Image Forensics for Copy and Paste Tampering 计算机科学, 2019, 46(6A): 380-384.
[6]	邵进达, 杨帅, 程琳. 改进SIFT算法结合两级特征匹配的无人机图像匹配算法 UAV Image Matching Algorithm Based on Improved SIFT Algorithm and Two-stage Feature Matching 计算机科学, 2019, 46(6): 316-321. https://doi.org/10.11896/j.issn.1002-137X.2019.06.048
[7]	刘朝霞,邵峰,景雨,祁瑞华. 基于视觉约束能量最小化的特征点匹配算法 Feature Matching Algorithm Based on Visual Feature Constrained Energy Minimization 计算机科学, 2018, 45(5): 228-231. https://doi.org/10.11896/j.issn.1002-137X.2018.05.039
[8]	龚安,费凡,郑君. 基于卷积神经网络的多人行为识别方法 Multi-person Behavior Recognition Method Based on Convolutional Neural Networks 计算机科学, 2018, 45(2): 306-311. https://doi.org/10.11896/j.issn.1002-137X.2018.02.053
[9]	刘川熙,赵汝进,刘恩海,洪裕珍. 基于RANSAC的SIFT匹配阈值自适应估计 Estimate Threshold of SIFT Matching Adaptively Based on RANSAC 计算机科学, 2017, 44(Z6): 157-160. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.036
[10]	王怡,徐文迪,余慧斌,郑河荣,潘翔. 显著性特征约束的交互式协同分割 Interactive Image Co-segmentation with Saliency Constraint 计算机科学, 2017, 44(Z11): 269-272. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.057
[11]	王健,白鹤翔,李德玉. 基于SIFT和非参贝叶斯的高分辨率遥感影像地物识别算法 High Resolution Remote Sensing Image Object Recognition Algorithm Based on SIFT and Non-parametric Bayes 计算机科学, 2017, 44(1): 289-294. https://doi.org/10.11896/j.issn.1002-137X.2017.01.053
[12]	甘威,张素文,雷震,李怡凡. 移动智能终端的SIFT特征检测并行算法 SIFT Feature Extraction Parallel Algorithm on Mobile Device 计算机科学, 2016, 43(Z6): 165-167. https://doi.org/10.11896/j.issn.1002-137X.2016.6A.038
[13]	李昆仑,孙硕. 基于改进SIFT算法的图像复制粘贴篡改检测 Image Copy-Paste Tampering Detection Based on Improved SIFT Algorithm 计算机科学, 2016, 43(Z6): 179-183. https://doi.org/10.11896/j.issn.1002-137X.2016.6A.042
[14]	瞿中,林嗣鹏,鞠芳蓉. 一种改进的降低扭曲误差的快速图像拼接算法 Improved Algorithm of Fast Image Stitching by Reducing Panoramic Distortion 计算机科学, 2016, 43(5): 279-282. https://doi.org/10.11896/j.issn.1002-137X.2016.05.053
[15]	任伟建,王子维,康朝海. 基于改进SIFT算法的无人机遥感图像匹配 Remote Sensing Image of UAV Registration Based on Improved SIFT Algorithm 计算机科学, 2015, 42(Z11): 179-182.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于改进SIFT的多光谱图像匹配算法

Multispectral Image Matching Algorithm Based on Improved SIFT

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0