Interest Point Detection for Multispectral Remote Sensing Image Using Phase Congruency in Illumination Space

doi:10.11947/j.AGCS.2016.20150030

Acta Geodaetica et Cartographica Sinica ›› 2016, Vol. 45 ›› Issue (2): 178-185.doi: 10.11947/j.AGCS.2016.20150030

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Interest Point Detection for Multispectral Remote Sensing Image Using Phase Congruency in Illumination Space

CHEN Min^{1 2}, ZHU Qing¹, ZHU Jun¹, XU Zhu^{1 3 4}, HUANG Lanxin¹

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
2. Sichuan Engineering Research Center for Emergency Mapping & Disaster Reduction, Chengdu 610041, China;
3. Collaborative Innovation Center for Rail Transport Safety, Chengdu 610031, China;
4. State-Province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Chengdu 610031, China

Received:2015-01-20 Revised:2015-08-29 Online:2016-02-20 Published:2016-02-29
Supported by:
The National Natural Science Foundation of China (Nos. 41471320;41501492);The Science and Technology Program of Sichuan Province of China (Nos. 2014SZ0106;2015SZ0046);The Open Research Fund by Sichuan Engineering Research Center for Emergency Mapping & Disaster Reduction (No. K2015B006);The Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. (14) Key 03);The Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13092)

Abstract

Abstract: A robust interest point detection algorithm based on illumination space and phase congruency is proposed in this paper. Firstly, image illumination space is constructed by using a parameters adaptive method. Secondly, a phase congruency based interest point detection algorithm is adopted to compute candidate points in illumination space. Then, all interest point candidates are mapped back to the original image and a non-maximum suppression step is added to find final interest points. Finally, the feature scale values of all interest points are calculated based on the Laplacian function. The proposed algorithm combines the advantages of illumination space and phase congruency, which makes the proposed method robust to the radiation variation of multispectral images. The experimental results show that the proposed method performs better than other traditional methods in feature repeatability rate and repeated features number.

Key words: phase congruency, illumination space, multispectral remote sensing image, interest point detection

CLC Number:

P237

CHEN Min, ZHU Qing, ZHU Jun, XU Zhu, HUANG Lanxin. Interest Point Detection for Multispectral Remote Sensing Image Using Phase Congruency in Illumination Space[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(2): 178-185.

References

[1] HARRIS C, STEPHENS M. A Combined Corner and Edge Detector[C]//Proceedings of the 4th Alvey Vision Conference. Plessey Research Roke Manor: The Plessey Company, 1988: 147-152.
[2] REISFELD D, WOLFSON H, YESHURUN Y. Context-free Attentional Operators: The Generalized Symmetry Transform[J]. International Journal of Computer Vision, 1995, 14(2): 119-130.
[3] 王青松, 赵西安, 吕京国, 等. 基于高斯差分的改进Harris特征点提取算法[J]. 测绘科学, 2014, 39(4): 119-122, 134. WANG Qingsong, ZHAO Xi'an, LV Jingguo, et al. Feature Points Extraction with Improved Harris Algorithm Based on Difference of Gaussian[J]. Science of Surveying and Mapping, 2014, 39(4): 119-122, 134.
[4] SMITH S, BRADT J M. SUSAN: A New Approach to Low Level Image Processing[J]. International Journal of Computer Vision, 1997, 23(1): 45-78.
[5] 王巍, 赵红蕊. 面向影像匹配的SUSAN角点检测[J]. 遥感学报, 2011, 15(5): 940-956. WANG Wei, ZHAO Hongrui. The Improvement of SUSAN for Image Matching[J]. Journal of Remote Sensing, 2011, 15(5): 940-956.
[6] 陈敏, 邵振峰. 一种稳健的高效角点特征检测方法提取变换[J]. 武汉大学学报(信息科学版), 2013, 38(10): 1142-1147. CHEN Min, SHAO Zhenfeng. A Robust and Efficient Feature Extract Transform[J]. Geomatics and Information Science of Wuhan University, 2013, 38(10): 1142-1147.
[7] LOWE D G. Distinctive Image Features from Scale-invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[8] BAY H, ESS A, TUYTELAARS T, et al. Speeded-up Robust Features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.
[9] ALAHI A, ORTIZ R, VANDERGHEYNST P. FREAK: Fast Retina Keypoint[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012: 510-517.
[10] HASAN M, JIA Xiuping, ROBLES-KELLY A, et al. Multi-spectral Remote Sensing Image Registration via Spatial Relationship Analysis on SIFT Keypoints[C]//Proceedings of IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI: IEEE, 2010: 1011-1014.
[11] VURAL M, YARDIMCI Y, TEMIZEL A. Registration of multispectral Satellite Images with Orientation-restricted SIFT[C]//Proceedings of IEEE International, IGARSS 2009, Geoscience and Remote Sensing Symposium. Cape Town: IEEE, 2009, 3: III-243-III-246.
[12] 戴激光, 宋伟东, 贾永红, 等. 一种新的异源高分辨率光学卫星遥感影像自动匹配算法[J]. 测绘学报, 2013, 42(1): 80-86. DAI Jiguang, SONG Weidong, JIA Yonghong, et al. A New Automatically Matching Algorithm for Multi-source High Resolution Optical Satellite Images[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 80-86.
[13] 戴激光, 宋伟东, 李玉. 渐进式异源光学卫星影像SIFT匹配方法[J]. 测绘学报, 2014, 43(7): 746-752. DAI Jiguang, SONG Weidong, LI Yu. Progressive SIFT Matching Algorithm for Multi-source Optical Satellite Images[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(7): 746-752.
[14] 张谦, 贾永红, 胡忠文. 多源遥感影像配准中的SIFT特征匹配改进[J]. 武汉大学学报(信息科学版), 2013, 38(4): 455-459. ZHANG Qian, JIA Yonghong, HU Zhongwen. An Improved SIFT Algorithm for Multi-source Remote Sensing Image Registration[J]. Geomatics and Information Science of Wuhan University, 2013, 38(4): 455-459.
[15] 袁修孝, 李然. 带匹配支持度的多源遥感影像SIFT匹配方法[J]. 武汉大学学报(信息科学版), 2012, 37(12): 1438-1442. YUAN Xiuxiao, LI Ran. A SIFT Image Match Method with Match-support Measure for Multi-source Remotely Sensed Images[J]. Geomatics and Information Science of Wuhan University, 2012, 37(12): 1438-1442.
[16] KOVESI P. Image Features from Phase Congruency[J]. Journal of Computer Vision Research, 1999, 1(3): 2-27.
[17] KOVESI P. Phase Congruency Detects Corners and Edges[C]//Proceedings of the 7th Digital Image Computing: Techniques and Applications. Sydney: [s.n.], 2003: 309-318.
[18] 叶沅鑫, 单杰, 熊金鑫, 等. 一种结合SIFT和边缘信息的多源遥感影像匹配方法[J]. 武汉大学学报(信息科学版), 2013, 38(10): 1148-1151, 1260. YE Yuanxin, SHAN Jie, XIONG Jinxin, et al. A Matching Method Combining SIFT and Edge Information for Multi-source Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2013, 38(10): 1148-1151, 1260.
[19] 叶沅鑫, 单杰, 彭剑威, 等. 利用局部自相似进行多光谱遥感图像自动配准[J]. 测绘学报, 2014, 43(3): 268-275. YE Yuanxin, SHAN Jie, PENG Jianwei, et al. Automated Multispectral Remote Sensing Image Registration Using Local Self-similarity[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(3): 268-275.
[20] 李明, 李德仁, 范登科, 等. 利用PC-SIFT的多源光学卫星影像自动配准方法[J]. 武汉大学学报(信息科学版), 2015, 40(1): 64-70. LI Ming, LI Deren, FAN Dengke, et al. An Automatic PC-SIFT-based Registration of Multi-source Images from Optical Satellites[J]. Geomatics and Information Science of Wuhan University, 2015, 40(1): 64-70.
[21] MIKOLAJCZYK K, TUYTELAARS T, SCHMID C, et al. A Comparison of Affine Region Detectors[J]. International Journal of Computer Vision, 2005, 65(1-2): 43-72.

Interest Point Detection for Multispectral Remote Sensing Image Using Phase Congruency in Illumination Space

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