Combinational color histogram and LBP textural features for remote sensing image segmentation

doi:10.6046/gtzyyg.2017.03.05

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Abstract A novel segmentation method combining color histogram with LBP textural features for segmentation of high-resolution remote sensing images is presented. This method starts with an adaptive marker-based watershed algorithm to obtain an initial segmentation result, and the markers are constructed by dual-threshold joint segmentation of the gradient image. And then a regional similarity indicator combining color histogram with LBP textural features is adopted to guide regional merging procedure and obtain the final result. Comparative experiments on high-resolution remote sensing images have proved the effectiveness of the method.

Keywords hyperspectral alteration Baiyanghe uranium deposit mineral mapping

Issue Date: 15 August 2017

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	ZHANG Chuan
	YE Fawang
	XU Qingjun
	LIU Hongcheng
	MENG Shu

Cite this article:

ZHANG Chuan,YE Fawang,XU Qingjun, et al. Combinational color histogram and LBP textural features for remote sensing image segmentation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 32-40.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.03.05 OR https://www.gtzyyg.com/EN/Y2017/V29/I3/32

[1] 冷美萍,鲍苏苏.基于色调直方图和区域合并的彩色图像分割算法[J].计算机应用,2010,30(3):653-656.
Leng M P,Bao S S.Color image segmentation algorithm based on hue level histogram and region merging[J].Journal of Computer Applications,2010,30(3):653-656.
[2] 王宇,陈殿仁,沈美丽,等.基于形态学梯度重构和标记提取的分水岭图像分割[J].中国图象图形学报,2008,13(11):2176-2180.
Wang Y,Chen D R,Shen M L,et al.Watershed segmentation based on morphological gradient reconstruction and marker extraction[J].Journal of Image and Graphics,2008,13(11):2176-2180.
[3] Gonzalez R C,Woods R E,Eddins S L.数字图像处理(MATLAB版)[M].阮秋琦译.北京:电子工业出版社,2005.
Gonzalez R C,Woods R E,Eddins S L.Digital Image Processing Using MATLAB[M].Translated by Ruan Q Q.Beijing:Electronic Industry Press,2005.
[4] 陈波,张友静,陈亮.标记分水岭算法及区域合并的遥感图像分割[J].国土资源遥感,2007(2):35-38.doi:10.6046/gtzyyg.2007.02.09"> doi:10.6046/gtzyyg.2007.02.09.
Chen B,Zhang Y J,Chen L.Segmentation of the remote sensing image based on method of labeling watershed algorithm and regional merging[J].Remote Sensing for Land and Resources,2007(2):35-38.doi:10.6046/gtzyyg.2007.02.09"> doi:10.6046/gtzyyg.2007.02.09.
[5] 张桂峰.粒度理论下的多尺度遥感影像分割[D].武汉:武汉大学,2010.
Zhang G F.Multi-scale Remote Sensing Image Segmentation Based on Granularity Theory[D].Wuhan:Wuhan University,2010.
[6] 王鑫,罗斌,宁晨.一种基于小波和分水岭算法的图像分割方法[J].计算机技术与发展,2006,16(1):17-19,22.
Wang X,Luo B,Ning C.Image segmentation method based on wavelet and watersheds algorithm[J].Computer technology and Development,2006,16(1):17-19,22.
[7] 巫兆聪,胡忠文,欧阳群东.一种区域自适应的遥感影像分水岭分割算法[J].武汉大学学报(信息科学版),2011,36(3):293-296.
Wu Z C,Hu Z W,Ouyang Q D.A regional adaptive segmentation algorithm for remote sensing image[J].Geomatics and Information Science of Wuhan University,2011,36(3):293-296.
[8] 张平,王文伟,吴丽芸.基于均匀性图分水岭变换及两步区域合并的彩色图像分割[J].计算机应用,2006,26(6):1378-1380.
Zhang P,Wang W W,Wu L Y.Color image segmentation based on watershed transformation upon homogeneity and two-step region merging[J].Computer Applications,2006,26(6):1378-1380.
[9] Wyszecki G,Stiles W S.Color Science:Concepts and Methods,Quantitative Data and Formulae[M].2nd ed.New York:John Wiley & Sons,2000.
[10] 林开颜,吴军辉,徐立鸿.彩色图像分割方法综述[J].中国图象图形学报,2005,10(1):1-10.
Lin K Y,Wu J H,Xu L H.A survey on color image segmentation techniques[J].Journal of Image and Graphics,2005,10(1):1-10.
[11] 陈秋晓,陈述彭,周成虎.基于局域同质性梯度的遥感图像分割方法及其评价[J].遥感学报,2006,10(3):357-365.
Chen Q X,Chen S P,Zhou C H.Segmentation approach for remote sensing images based on local homogeneity gradient and its evaluation[J].Journal of Remote Sensing,2006,10(3):357-365.
[12] 张长青,葛文英,刘国英.一种基于区域分级合并的彩色图像分割方法[J].计算机工程与应用,2012,48(17):203-206,232.
Zhang C Q,Ge W Y,Liu G Y.Color image segmentation algorithm based on multi-level region merging[J].Computer Engineering and Applications,2012,48(17):203-206,232.
[13] Ning J F,Zhang L,Zhang D,et al.Interactive image segmentation by maximal similarity based region merging[J].Pattern Recognition,2010,43(2):445-456.
[14] Ojala T,Pietikainen M,Harwood D.A comparative study of texture measures with classification based on feature distributions[J].Pattern Recognition,1996,29(1):51-59.
[15] 巫兆聪,胡忠文,张谦,等.结合光谱、纹理与形状结构信息的遥感影像分割方法[J].测绘学报,2013,42(1):44-50.
Wu Z C,Hu Z W,Zhang Q,et al.On combining spectral,textural and shape features for remote sensing image segmentation[J].Acta Geodaetica et Cartographica Sinica,2013,42(1):44-50.
[16] Alpert S,Galun M,Basri R,et al.Image segmentation by probabilistic bottom-up aggregation and cue integration[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Minneapolis,MN:IEEE,2007:1-8.
[17] Hoyer P O.Non-negative matrix factorization with sparseness constraints[J].The Journal of Machine Learning Research,2004,5:1457-1469.
[18] 蒋志勇,陈晓玲,秦前清.一种基于淹没的分水岭变换算法[J].武汉大学学报(信息科学版),2009,34(5):509-513.
Jiang Z Y,Chen X L,Qin Q Q.Watershed transform based on flooding[J].Geomatics and Information Science of Wuhan University,2009,34(5):509-513.

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