基于监督分类的震后高分辨率影像倒塌房屋快速识别
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摘要
针对监督分类中样本设计与选取、关键特征提取两个关键环节,设计了多种不同样本提取方法和多种典型特征参数组合,对海地震后高分辨率影像倒塌房屋快速提取进行分析研究。结果表明,以倒塌样本与屋角样本作为训练样本,以灰度均值和灰度共生矩阵逆差矩作为参数组合,能够保证较好提取精度的同时,最大限度减少人工样本选取工作量,提高倒塌房屋快速提取效率。最后以该分类方法对玉树震后高分辨率影像的倒塌房屋进行自动识别,识别结果良好,进一步检验了该分类方法的有效性。
In this paper,we study two core steps in supervised methods including design of training sample collection,and feature extraction.Various methods on sample collection and feature extraction are compared and analyzed with the post-earthquake high-resolution images in Haiti.The experiment indicates that,by using samples of house corners alone as training samples,and combination of average intensity and inverse difference moment in gray level co-occurrence matrices as features,an effective method of collapsed building extraction is reached.Also the manual work of sample collection is greatly relieved,which in turn improve the efficiency of collapsed building extraction.We apply the method to the collapsed building extraction of Yushu post-earthquake high-resolution image,the satisfied results are obtained,which validates the extraction method further.
In this paper,we study two core steps in supervised methods including design of training sample collection,and feature extraction.Various methods on sample collection and feature extraction are compared and analyzed with the post-earthquake high-resolution images in Haiti.The experiment indicates that,by using samples of house corners alone as training samples,and combination of average intensity and inverse difference moment in gray level co-occurrence matrices as features,an effective method of collapsed building extraction is reached.Also the manual work of sample collection is greatly relieved,which in turn improve the efficiency of collapsed building extraction.We apply the method to the collapsed building extraction of Yushu post-earthquake high-resolution image,the satisfied results are obtained,which validates the extraction method further.
引文
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[2]雷莉萍,刘良云,张丽,等.汶川地震房屋倒塌的遥感监测与分析[J].遥感学报,2010,14(2):333-344.
[3]郭华东,鹿琳琳,马建文,等.一种改进的地震灾害倒塌房屋遥感信息自动识别方法[J].科学通报,2009,54(17):2581-2585.
[4]柳稼航,单新建,尹京苑.遥感图象自动识别城市震害房屋-以2001年印度库奇地震和1976年唐山地震为例[J].地震学报,2004,26(6):623-633.
[5]Turker M,Emre S.Building-based damage detection due to earthquake using the watershed segmentation of the post-e-vent aerial images[J].International Journal of Remote Sensing,2008,29(11):3073-3089.
[6]Vu,T.T.,Ban,Y..Context-based mapping of damaged buildings from high-resolution optical satellite images[J].Inter-national Journal of Remote Sensing,2010(31):3411-3425.
[7]Liwei LI,Zuchuan LI,Rui ZHANG,et al..Collapsed buildings extraction using morphological profiles and texture statis-tics-A case study in the 5.12Wenchuan earthquake[C]//IGARSS′10.
[8]李祖传,马建文,张睿,等.基于纹理与形态特征的地震倒塌房屋信息自动提取研究[J].武汉大学学报〈信息科学版〉,待刊.
[9]张景发,谢礼立,陶夏新.建筑物震害遥感图像的变化检测与震害评估[J].自然灾害学报,2002,11(2):59-64.
[10]Rafael C.Gonzalez,Richard E.Woods.数字图像处理[M].北京:机械工业出版社,2007:427,540-542.
[11]ROBERT M.HARALICK,K.SHANMUGAM,ITSHAK DINSTEIN.Texture features for image classification[J].IEEE Transactions on System,Man,and Cybernetics,1973,SMC-3(6):610-621.
[12]Leen-Kiat Soh,Costas Tsatsoulis.Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(2):780-795.
[13]白雪冰,王克奇,王辉.基于灰度共生矩阵的木材纹理分类方法的研究[J].哈尔滨工业大学学报,2005,37(12):1667-1670.
[14]边肇祺.模式识别(第二版)[M].清华大学出版社,2000:9-42.
[15]C.Elkan.Boosting and Naive Bayesian learning[R].In Technical Report CS97,Dept.of Computer Science and Engineer-ing,Univ.Calif.at San Diego,1997.
[16]Baraldi,A.,Parmiggiani,F..An investigation of the textural characteristics associated with gray level cooccurrence ma-trix statistical parameters[J].IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):293-304.
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