Remote sensing image classification based on G statistics of object histogram

doi:10.6046/gtzyyg.2016.04.11

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Abstract

In order to make full use of the spectral feature of the object, this paper proposes a classification method for remote sensing image based on G statistics of the object histogram. Image objects were obtained by multi-resolution image segmentation method. Then training objects were chosen from these objects. The histogram of the object was obtained with the adaptive gray level according to the spectral property. G statistics was used to measure the histogram distance between test object and training object which describes the heterogeneity of two objects. Minimum distance classifier was employed to get the image classification result. The experiment on the remote sensing image shows that the proposed method can improve the accuracy of the classification.

Keywords LiDAR 3D reconstruction CSG primitive decomposition primitive recognition contour cluster contour reconstruction

TP751.1

Issue Date: 20 October 2016

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	ZHA Dajian
	LI Lelin
	JIANG Wangshou
	HAN Yongshun

Cite this article:

ZHA Dajian,LI Lelin,JIANG Wangshou, et al. Remote sensing image classification based on G statistics of object histogram[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 71-76.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.04.11 OR https://www.gtzyyg.com/EN/Y2016/V28/I4/71

[1] 赵红蕊,阎广建,邓小炼,等.一种简单加入空间关系的实用图像分类方法[J].遥感学报,2003,7(5):358-363. Zhao H R,Yan G J,Deng X L,et al.A classification method based on spatial information[J].Journal of Remote Sensing,2003,7(5):358-363.
[2] Aguilar M A,Saldaña M M,Aguilar F J.GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments[J].International Journal of Remote Sensing,2013,34(7):2583-2606.
[3] 杨耘,徐丽,颜佩丽.条件随机场框架下基于随机森林的城市土地利用/覆盖遥感分类[J].国土资源遥感,2014,26(4):51-55.doi:10.6046/gtzyyg.2014.04.09. Yang Y,Xu L,Yan P L.Urban land use/cover classification of remote sensing using random forests under the framework of conditional random fields[J].Remote Sensing for Land and Resources,2014,26(4):51-55.doi:10.6046/gtzyyg.2014.04.09.
[4] Doxani G,Karantzalos K,Tsakiri-Strati M. Monitoring urban changes based on scale-space filtering and object-oriented classification[J].International Journal of Applied Earth Observation and Geoinformation,2012,15:38-48.
[5] 李莎,倪维平,严卫东,等.基于选权迭代估计与非监督分类的多光谱图像变化检测[J].国土资源遥感,2014,26(4):34-40.doi:10.6046/gtzyyg.2014.04.06. Li S,Ni W P,Yan W D,et al.Change detection of multi-spectral images based on iterative estimation with weight selection and unsupervised classification[J].Remote Sensing for Land and Resources,2014,26(4):34-40.doi:10.6046/gtzyyg.2014.04.06.
[6] 孙永军,童庆禧,秦其明.利用面向对象方法提取湿地信息[J].国土资源遥感,2008,20(1):79-82.doi:10.6046/gtzyyg.2008.01.18. Sun Y J,Tong Q X,Qin Q M.The object-oriented method for wetland information extraction[J].Remote Sensing for Land and Resources,2008,20(1):79-82.doi:10.6046/gtzyyg.2008.01.18.
[7] 周林滔,杨国范,赵福强,等.EMD与分形相结合的遥感影像水体信息提取方法[J].国土资源遥感,2014,26(4):41-45.doi:10.6046/gtzyyg.2014.04.07. Zhou L T,Yang G F,Zhao F Q,et al.Water information extraction from remote sensing image using EMD and fraction method[J].Remote Sensing for Land and Resources,2014,26(4):41-45.doi:10.6046/gtzyyg.2014.04.07.
[8] 周前祥,敬忠良.高光谱遥感图像联合加权随机分类器的设计与应用[J].测绘学报,2004,33(3):254-257. Zhou Q X,Jing Z L.Weighted combination random classifier of high spectral remote sensing image:Design and application[J].Acta Geodaetica et Cartographica Sinica,2004,33(3):254-257.
[9] 骆剑承,王钦敏,马江洪,等.遥感图像最大似然分类方法的EM改进算法[J].测绘学报,2002,31(3):234-239. Luo J C,Wang Q M,Ma J H,et al.The EM-based maximum likelihood classifier for remotely sensed data[J].Acta Geodaetica et Cartographica Sinica,2002,31(3):234-239.
[10] Maulik U,Chakraborty D.A self-trained ensemble with semisupervised SVM:An application to pixel classification of remote sensing imagery[J].Pattern Recognition,2011,44(3):615-623.
[11] 张友水,冯学智,阮仁宗,等.Kohonen神经网络在遥感影像分类中的应用研究[J].遥感学报,2004,8(2):178-184. Zhang Y S,Feng X Z,Ruan R Z,et al.Application of Kohonen network in RS image classification[J].Journal of Remote Sensing,2004,8(2):178-184.
[12] Tansey K,Chambers I,Anstee A,et al.Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas[J].Applied Geography,2009,29(2):145-157.
[13] 邓媛媛,巫兆聪,易俐娜,等.面向对象的高分辨率影像农用地分类[J].国土资源遥感,2010,22(4):117-121.doi:10.6046/gtzyyg.2010.04.24. Deng Y Y,Wu Z C,Yi L N,et al.Research on object-oriented classification of agricultural land based on high resolution images[J].Remote Sensing for Land and Resources,2010,22(4):117-121.doi:10.6046/gtzyyg.2010.04.24.
[14] 王琰,舒宁,龚龑,等.基于类别光谱变化规律的土地利用变化检测[J].国土资源遥感,2012,24(3):92-96.doi:10.6046/gtzyyg.2012.03.17. Wang Y,Shu N,Gong Y,et al.Land use change detection based on class spectral change rule[J].Remote Sensing for Land and Resources,2012,24(3):92-96.doi:10.6046/gtzyyg.2012.03.17.
[15] 陈秋晓,骆剑承,周成虎,等.基于多特征的遥感影像分类方法[J].遥感学报,2004,8(3):239-245. Chen Q X,Luo J C,Zhou C H,et al.Classification of remotely sensed imagery using multi-features based approach[J].Journal of Remote Sensing,2004,8(3):239-245.
[16] 陈云浩,冯通,史培军,等.基于面向对象和规则的遥感影像分类研究[J].武汉大学学报:信息科学版,2006,31(4):316-320. Chen Y H,Feng T,Shi P J,et al.Classification of remote sensing image based on object oriented and class rules[J].Geomatics and Information Science of Wuhan University,2006,31(4):316-320.
[17] 蔡晓斌,陈晓玲,王涛,等.基于图斑空间关系的遥感专家分类方法研究[J]. 武汉大学学报:信息科学版,2006,31(4):321-324. Cai X B,Chen X L,Wang T,et al.Remote sensing expert classification method based on patch spatial relationship[J].Geomatics and Information Science of Wuhan University,2006,31(4):321-324.
[18] 陈杰,邓敏,肖鹏峰,等.粗糙集高分辨率遥感影像面向对象分类[J].遥感学报,2010,14(6):1139-1155. Chen J,Deng M,Xiao P F,et al.Rough set theory based object-oriented classification of high resolution remotely sensed imagery[J].Journal of Remote Sensing,2010,14(6):1139-1155.
[19] Ojala T,Pietikäinen M.Unsupervised texture segmentation using feature distributions[J].Pattern Recognition,1999,32(3):477-486.
[20] Wang A P,Wang S G,Lucieer A.Segmentation of multispectral high-resolution satellite imagery based on integrated feature distributions[J].International Journal of Remote Sensing,2010,31(6):1471-1483.

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