用于遥感图像拼接的改进SURF算法

doi:10.13229/j.cnki.jdxbgxb201705042

摘要/Abstract

摘要： 经典的SURF算法存在许多不足,如特征描述符维度高、运算量大,对于旋转和拍射视角变换角度过大时,匹配精度低等。针对以上问题,提出了一种改进算法,首先通过Hessian矩阵提取特征点,然后采用特征点圆形邻域进行特征描述,使用Haar小波响应为每个特征点建立描述符,同时计算邻域内归一化的灰度差分及二阶梯度,形成新的特征描述符,最后采用RANSAC算法剔除误匹配点。该算法不仅较经典SURF算法具有速度优势,同时充分利用了灰度信息和细节信息,具有更高的精度。实验结果表明:该算法对图像的模糊、光照差异、角度旋转、视场变换等均有良好的鲁棒性和稳定性。将该算法应用于遥感图像拼接,得到无明显几何移位、边缘衔接良好的拼接图像。该算法是一种耗时短、精度高的图像配准算法,能够满足遥感图像拼接对配准的要求。

关键词: 计算机应用, 图像配准, 特征提取, SURF算法, 二阶梯度

Abstract: To overcome the redundant feature descpriptor, high computational complexity, low matching precision when the angle of rotation or view spends greatly in image registration method based on SURF algorithm, an improved SURF algorithm is proposed. First, the feature points are extracted using Hessian matrix. Then, the feature descriptor for each keypoint in the circular neighborhood is constructed using Haar wavelet response; meanwhile, the normalized gray values difference and second-order gradient of this region are computed. Finally, RANSAC algorithm is applied to eliminate false matches. This method not only performs faster than SURF algorithm, but also fully employs the image gray information and details to acquire higher accuracy. Results indicate that the proposed method has strong robustness and stability for blur, illumination difference, angle rotation and viewpoint change. A well-edge mosaic image is obtained without obvious geometric misalignment in the remote sensing image mosaicking process. This method is an effective image registration algorithm with high-speed and precision, and it satisfies the need of registration in the remote sensing image mosaic.

Key words: computer application, image registration, feature extraction, SURF algorithm, second-order gradient

中图分类号:

TP391

董强, 刘晶红, 周前飞. 用于遥感图像拼接的改进SURF算法[J]. 吉林大学学报(工学版), 2017, 47(5): 1644-1652.

DONG Qiang, LIU Jing-hong, ZHOU Qian-fei. Improved SURF algorithm used in image mosaic[J]. 吉林大学学报(工学版), 2017, 47(5): 1644-1652.

参考文献

[1] 席海峰,田超.基于 SVR 的宽基线图像匹配方法[J]. 重庆邮电大学学报:自然科学版, 2013, 25(2):197-202.
Xi Hai-feng, Tian Chao. Wide baseline image matching using support vector regression[J]. Journal of Chongqing University of Posts & Telecommunications(Natural Science Edition), 2013, 25(2):197-202.
[2] 杨光, 田地, 李军,等. 基于投影特征的快速图像匹配方法[J]. 吉林大学学报:工学版, 2010, 40(5):1340-1344.
Yang Guang,Tian Di,Li Jun,et al. Fast image matching method based on projective feature[J]. Journal of Jilin University(Engineering and Technology Edition),2010, 40(5):1340-1344.
[3] 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 Xian-chuan,Lyu Zhong-hua,Hu Dan. Review of remote sensing image registration technique[J].Optics and Precision Engineering, 2013, 21(11):2960-2972.
[4] 颜雪军,赵春霞,袁夏.一种鲁棒的基于图像对比度的局部特征描述方法[J]. 电子与信息学报, 2014,36(4):882-887.
Yan Xue-jun,Zhao Chun-xia,Yuan Xia. A robust local feature descriptor based on image contrast[J]. Journal of Electronics & Information Technology, 2014,36(4):882-887.
[5] 安建妮,刘贵喜. 利用特征点配准和变换参数自动辨识的图像拼接算法[J]. 红外与激光工程, 2011, 40(3): 564-569.
An Jian-ni, Liu Gui-xi. Image mosaic algorithm base on feature points matching and automatic transform parameters identifying[J]. Infrared and Laser Engineering, 2011, 40(3): 564-569.
[6] Wang Wei-xing,Cao Ting,Liu Sheng,et al. Remote sensing image automatic registration on multi-scale harris-laplacian[J]. Indian Soc Remote Sens,2015,43(3):501-511.
[7] Olszewska J I. Active contour based optical character recognition for automated scene understanding[J]. Neurocomputing, 2015, 161:65-71.
[8] Zhang Jian-wei, Huang Da-cheng, Gui Jiang-qin, et al. 2D registration based on contour matching for partial matching images[J]. Journal of Central South University, 2014, 21(12):4553-4562.
[9] Sutour C, Aujol J F, Deledalle C A, et al. Edge-based multi-modal registration and application for night vision devices[J]. Journal of Mathematical Imaging and Vision, 2015, 53(2):131-150.
[10] Zhang Han, Ni Wei-ping, Yan Wei-dong. Robust SAR image registration based on edge matching and refined coherent point drift[J]. IEEE Geoscience and Remote Sensing Letters, 2015,12:2115-2119.
[11] Lowe D G. Object recognition from local scaleinvariant features[C]∥Proceedings of the 7th International Conference on Computer Vision,Corfu,Greece,1999:1150-1157.
[12] Lowe D G. Distinctive Image features from scale-invariant key points[J].International Journal of Computer Vision,2004,60(2):91-110.
[13] 汪松, 王俊平, 万国挺,等. 基于SIFT算法的图像匹配方法[J]. 吉林大学学报:工学版, 2013(增刊1):279-282.
Wang Song,Wang Jun-ping,Wan Guo-ting,et al. Image matching method based on SIFT algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2013(Sup.1):279-282.
[14] 曾峦, 王元钦, 谭久彬.改进的SIFT特征提取和匹配算法[J]. 光学精密工程, 2011,19 (6): 1391-1397.
Zeng Luan, Wang Yuan-qin, Tan Jiu-bin. Improved algorithm for SIFT feature extraction and matching[J]. Optics and Precision Engineering, 2011,19 (6): 1391-1397.
[15] Ke Y, Sukthankar R. PCA-SIFT:a more distinctive representation for local image descriptors[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,Washington DC, USA,2004:506-513.
[16] Bay H,Tuytelaars T,Gool L.SURF:speeded up robust features[C]∥Proceedings of the 9th European Conference Computer Vision,IEEE,2006:404-417.
[17] 谷宗运,谭红春,殷云霞,等. 基于SURF和改进的RANSAC算法的医学图像配准[J].中国医学影像学杂,2014, 22(6): 470-475, 480.
Gu Zong-yun,Tan Hong-chun,Yin Yun-xia,et al.Medical image registration based on SURF and improved RANSAC algorithm[J]. Chinese Journal of Medical Imaging,2014, 22(6): 470-475, 480.
[18] Misra I, Moorthi S M, Dhar D, et al. An automatic satellite image registration technique based on harris corner detection and random sample consensus (RANSAC) outlier rejection model[C]∥International Conference on Recent Advances in Information Technology, IEEE, 2012:68-73.
[19] Chen Q,Montesinos P,Sun Q S,et al. Adaptive total variation denoising based on difference curvature[J].Image and Vision Computing,2010,28:298-306.
[20] Zhang R J,Zhang J Q,Yang C.Image registration approach based on SURF[J].Infrared and Laser Engineering,2009,38(1):160-165.

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