High spatial resolution remote sensing imagery edge extraction based on multi-direction wavelet transform

doi:10.6046/gtzyyg.2019.01.02

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Abstract

In view of the low resolution in direction of wavelet transform,the rich geometric structure of high spatial resolution remote sensing imagery (HSR) and the existence of edge in various directions which causes shortcomings in edge extraction of wavelet transform on objects with complex geometric structure, the authors proposed a HRS image edge extraction method of multi-direction wavelet transform based on Directionlet theory and modulus maximum method. The method first decomposes original image based lattice to obtain one-dimensional line set, then carries out wavelet transform and obtains the high frequency directional sub-band by restore image format. The edge result is obtained by using improved module maximum and the dual threshold method. Finally, the mathematical morphology is used to refine and connect edge results. Experiment result shows that the proposed method can get more complete edge compared with traditional method and standard two-dimensional wavelet transform.

Keywords edge extraction wavalet transform Directionlet transform modulus maximum method

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Corresponding Authors: Jiannong CAO E-mail: caojiannong@126.com

Issue Date: 15 March 2019

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	Junjun LI
	Jiannong CAO
	Juan LIAO
	Beibei CHENG

Cite this article:

Junjun LI,Jiannong CAO,Juan LIAO, et al. High spatial resolution remote sensing imagery edge extraction based on multi-direction wavelet transform[J]. Remote Sensing for Land & Resources, 2019, 31(1): 8-15.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.02 OR https://www.gtzyyg.com/EN/Y2019/V31/I1/8

Fig.1 Directional wavelet transform of HSR image

Fig.2 Lattice and co-line

Fig.3 Wavelet transform results of HSR image in different directions

Fig.4 Multi-direction gradient components

Fig.5 Position relation of direction of the two maximum gradient components

Fig.6 HSR image and result of non-maximum suppression

Fig.7 HSR image edge extraction flow chart based on multi-direction wavelet transform

Fig.8 Results of experiment

[1]	谭媛, 黄辉先, 徐建闽 , 等. 基于改进Sobel算子的遥感图像道路边缘检测方法[J]. 国土资源遥感, 2016,28(3):7-11.doi: 10.6046/gtzyyg.2016.03.02. doi: 10.6046/gtzyyg.2016.03.02 url: http://www.cqvip.com/QK/91397X/201603/669995275.html
[1]	Tan Y, Huang H X, Xu J M , et al. Road edge detection from remote sensing image based on improved Sobel operator[J]. Remote Sensing for Land and Resources, 2016,28(3):7-11.doi: 10.6046/gtzyyg.2016.03.02.
[2]	白婷婷, 邓彩霞, 耿英 . 基于小波变换与Canny算子融合的图像边缘检测方法[J]. 哈尔滨理工大学学报, 2010,15(1):44-47,51. doi: 10.3969/j.issn.1007-2683.2010.01.010 url: http://d.wanfangdata.com.cn/Periodical/heblgdxxb201001010
[2]	Bai T T, Deng C X, Geng Y . Image edge detection based on wavelet transform and Canny operator[J]. Journal of Harbin University of Science and Technology, 2010,15(1):44-47,51.
[3]	Canny J . A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986,8(6):679-698. doi: 10.1109/TPAMI.1986.4767851 url: http://dl.acm.org/citation.cfm?id=11274.11275
[4]	Zeng J, Li D. An improved Canny edge detector against impulsive noise based on CIELAB space [C]//International Symposium on Intelligence Information Processing and Trusted Computing. Huanggang:IEEE, 2010: 520-523.
[5]	Guo L Y, Nan J C. Canny edge detection algorithm based on wavelet transform and RAMF [C]//International Conference on Computational Problem-Solving.Lijiang:IEEE, 2010: 344-346.
[6]	李俊山, 马颖, 赵方舟 , 等. 改进的Canny图像边缘检测算法[J]. 光子学报, 2011,40(s1):50-54. doi: 10.3788/gzxb201140s1.0050 url: http://www.cqvip.com/QK/93651X/2011S1/1003578450.html
[6]	Li J S, Ma Y, Zhao F Z , et al. A novel arithemtic of image edge detection of Canny operator[J]. Acta Photonica Sinica, 2011,40(s1):50-54.
[7]	赵凌, 张祖荫, 郭伟 . 基于数学形态学的毫米波图像边缘检测方法[J]. 国土资源遥感, 2006,18(4):19-22.doi: 10.6046/gtzyyg.2006.04.05. doi: 10.3969/j.issn.1001-070X.2006.04.005 url: http://www.cqvip.com/QK/91397X/200604/23411210.html
[7]	Zhao L, Zhang Z Y, Guo W . A mathematical morphological for edge detection in millimeter wave radiometric images[J]. Remote Sensing for Land and Resources, 2006,18(4):19-22.doi: 10.6046/gtzyyg.2006.04.05.
[8]	Mallat S G . A theory for multiresolution signal decomposition:The wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989,11(7):674-693. doi: 10.1109/34.192463 url: http://ieeexplore.ieee.org/document/192463/
[9]	徐继友, 朱耆祥 . 小波变换在图象边缘提取中的应用[J]. 光电工程, 1998,25(4):28-34. url: http://www.cnki.com.cn/Article/CJFDTotal-GDGC804.003.htm
[9]	Xu J Y, Zhu Q X . The application of wavelet transform to image edge extracting[J]. Opto-Electronic Engineering, 1998,25(4):28-34.
[10]	高国荣, 刘冉, 羿旭明 . 一种改进的基于小波变换的图像边缘提取算法[J]. 武汉大学学报(理学版), 2005,51(5):615-619. doi: 10.3321/j.issn:1671-8836.2005.05.020 url: http://www.cqvip.com/QK/93869A/200505/20263164.html
[10]	Gao G R, Liu R, Yi X M . A revised image edge detection method based on wavelet[J]. Journal of Wuhan University (Natural and Science Edition), 2005,51(5):615-619.
[11]	章国宝, 叶桦, 陈维南 . 基于正交小波变换的多尺度边缘提取[J]. 中国图象图形学报, 1998,3(8):651-654. doi: 10.11834/jig.199808202 url: http://www.cnki.com.cn/Article/CJFDTotal-ZGTB808.006.htm
[11]	Zhang G B, Ye H, Chen W N . A multiscale edge detector based on orthogonal wavelet transform[J]. Journal of Image and Graphics, 1998,3(8):651-654.
[12]	Emmanuel J C, Donoho D L. Curvelets:A surprisingly effective nonadaptive representation for objects with edges[M] //Cohen A, Rabut C,Schumaker L.Curves and Surfaces Fitting.Nashville:Vanderbilt University Press, 2000: 105-120.
[13]	Candes E J . Ridgelets:Theory and Applications[D]. Stanford:Stanford University, 1998.
[14]	Do M N, Vetterli M . The Contourlet transform:An efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005,14(12):2091-2106. doi: 10.1109/TIP.2005.859376 pmid: 16370462 url: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1532309
[15]	Meyer F G, Coifman R R . Brushlets:A tool for directional image analysis and image compression[J]. Applied and Computational Harmonic Analysis, 1997,4(2):147-187. doi: 10.1006/acha.1997.0208 url: http://linkinghub.elsevier.com/retrieve/pii/S1063520397902086
[16]	赵振兵, 金思新, 刘亚春 . 基于NSCT的航拍绝缘子图像边缘提取方法[J]. 仪器仪表学报, 2012,33(9):2045-2052. doi: 10.3969/j.issn.0254-3087.2012.09.018 url: http://d.wanfangdata.com.cn/Periodical/yqyb201209018
[16]	Zhao Z B, Jin S X, Liu Y C . Aerial insulator image edge extraction method based on NSCT[J]. Chinese Journal of Scientific Instrument, 2012,33(9):2045-2052.
[17]	Jin R J, Yin J J, Zhou W, et al. Edge detection in polarimetric SAR images based on the nonsubsampled ontourlet transform [C]//IEEE Radar Conference(RadarCon).Arlington:IEEE, 2015: 0319-0323.
[18]	Zhou G Y, Cui Y, Chen Y L , et al. SAR image edge detection using Curvelet transform and Duda operator[J]. Electronics Letters, 2010,46(2):167-169. doi: 10.1049/el.2010.2888 url: https://digital-library.theiet.org/content/journals/10.1049/el.2010.2888
[19]	吴一全, 曹照清, 陶飞翔 . 结合多尺度几何分析和KICA的遥感图像变化检测[J]. 遥感学报, 2015,19(1):126-133. doi: 10.11834/jrs.20153254 url: http://www.cnki.com.cn/article/cjfdtotal-ygxb201501013.htm
[19]	Wu Y Q, Cao Z Q, Tao F X . Change detection of remote sensing images by multi-scale geometric analysis and KICA[J]. Journal of Remote Sensing, 2015,19(1):126-133.
[20]	Velisavljevic V, Beferull-Lozano B, Vetterli M , et al. Directionlets:Anisotropic multidirectional representation with separable filtering[J] IEEE Transactions on Image Processing, 2006,15(7):1916-1933. doi: 10.1109/TIP.2006.877076 pmid: 16830912 url: http://ieeexplore.ieee.org/document/1643699/
[21]	Chan T S, Yip R K K.Line detection algorithm [C]//13th International Conference on Pattern Recognition.Vienna:IEEE, 1996: 126-130.
[22]	江宇博, 刘波 . 小波模极大值法与数学形态学边缘检测细化结果[J]. 计算机测量与控制, 2017,25(3):165-168. doi: 10.16526/j.cnki.11-4762/tp.2017.03.045 url: http://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201703045.htm
[22]	Jiang Y B, Liu B . Wavelet modulus maxima method and mathematical morphology edge detection thinning results[J]. Computer Measurement and Control, 2017,25(3):165-168.
[23]	Zhang Z, Ma S L, Liu H , et al. An edge detection approach based on directional wavelet transform[J]. Computers and Mathematics with Applications, 2009,57(8):1265-1271. doi: 10.1016/j.camwa.2008.11.013 url: https://linkinghub.elsevier.com/retrieve/pii/S0898122109000492
[24]	张志强, 宋海生 . 应用Otsu改进Canny算子的图像边缘检测方法[J]. 计算机与数字工程, 2014,42(1):122-128,141. doi: 10.3969/j.issn.1672-9722.2014.01.032 url: http://d.wanfangdata.com.cn/Periodical/jsjyszgc201401032
[24]	Zhang Z Q, Song H S . Application of Otsu to improve image edge detection method in Canny algorithm[J]. Computer and Digital Engineering, 2014,42(1):122-128,141.
[25]	梁光明, 唐朝京, 刘东华 , 等. 基于分割评价的多层次自适应双阈值分割算法[J]. 电子学报, 2009,37(4):750-752,763. doi: 10.3321/j.issn:0372-2112.2009.04.014 url: http://d.wanfangdata.com.cn/Periodical/dianzixb200904014
[25]	Liang G M, Tang C J, Liu D H , et al. A multi-level and adaptive dual threshold segmentation algorithm based on evaluation[J]. Acta Electronica Sinica, 2009,37(4):750-752,763.
[26]	周绍光, 孙金彦, 凡莉 , 等. 高分辨率遥感影像的建筑物轮廓信息提取方法[J]. 国土资源遥感, 2015,27(3):52-58.doi: 10.6046/gtzyyg.2015.03.10. doi: 10.6046/gtzyyg.2015.03.10 url: http://www.cqvip.com/QK/91397X/201503/665774255.html
[26]	Zhou S G, Sun J Y, Fang L , et al. Extraction of building contour from high resolution images[J]. Remote Sensing for Land and Resources, 2015,27(3):52-58.doi: 10.6046/gtzyyg.2015.03.10.

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