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基于纹理特征的遥感影像监督分类 预览 被引量:1

The Research ON Remote Sensing Supervised Classification Based ON Texture Feature
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摘要 影像分类技术是遥感影像分析与解译的重要基础。纹理特征是影像的重要特征,本文主要实现基于纹理特征的遥感影像监督分类。首先对地物样本进行提取,通过样本训练统计各类地物纹理特征向量,建立纹理特征库;然后以各类地物的特征向量作为基准,采用最短距离分类器对影像进行分类;最后采用混淆矩阵对分类结果进行精度评定,并与ERDAS专业软件分类结果进行对比分析。实验证明,本分方法取得了与ERDAS软件相当的分类效果,从而验证本文方法的可靠性。 Image classification technology is the important foundation of remote sensing image analysis and interpretation.Texture feature is important feature,this paper realizes supervised classification based on texture feature.Firstly the features samples were extracted,and statistics all kinds of terrain texture feature vector by sample training,establishes texture feature library;And then selects all kinds of terrain feature vector as a benchmark,the minimum distance classifier for image classification;Finally makes precision evaluation to the classification results using the confusion matrix,and compares with classification results obtained by the ERDAS professional software.Experiment results show that the method proposed in this paper obtained equivalent classification results with ERDAS software,thus verifying the reliability of the method.
作者 洪洲 HONG Zhou(Tieling city mapping management office,Tieling 112000,China)
出处 《测绘与空间地理信息》 2013年第4期75-79,共5页 Geomatics & Spatial Information Technology
关键词 遥感图像 影像分类 纹理特征 监督分类 最短距离分类 Remote sensing image Image classification Texture feature Supervised classification Minimum distance classification
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  • 1Benediktsson J A, Pesaresi M, Amason K. Classifica- tion and feature extraction for remole sensing images from urban areas based on morphological transformations[ J ].Geoscience anti Remote Sensing, IEEE Transactions on, 2003, 41(9): 1940-1949. 被引量:1
  • 2Jimenez L O, Rivera-Medina J L, Rodrigucz-Dgaz E, e! al. Integration of spatial and spectral intbrmation by means of unsupervised extraction and classificati:m for homogenous objects applied to multispectral and hyper- spectraldata[ J]. Geoscience and Remote Sensing, IEEE Transactions on, 2005, 43 (4) : 844 - 851. 被引量:1
  • 3Stathakis D, Perakis K. Feature evolution for classifiea- tion of remotely sensed data[J]. Geoscienee and Renmte Sensing Letters, IEEE, 2007, 4(3) : 354 - 358. 被引量:1
  • 4Zhang L, ZhangL, Tao D, et al. On combining multiple features for hyperspectral remote sensing image classifi- cation [ J ]. Geoscienee and Remote Sensing, IEEE Transaetions on, 2012, 50(3): 879-893. 被引量:1
  • 5Kononenko I. Estimating attributes: analysis and exten- sions of RELIEF[ C ]//Machine Learaing: ECML - 94. Springer Berlin Heidelberg, 1994:171 - 182. 被引量:1
  • 6MarkoR S, Igor K. Theoretical and empirical analysis of ReliefF and RReliei[ J]. Journal of Machine Learning,2003,53(1 -2) :23 -69. 被引量:1
  • 7Ding C, Peng H. Minimum redundancy feature selection from microarray gene expression data [J]. Journal of bioinformatics and computational biology, 2005, 3 (02) : 185 - 205. 被引量:1
  • 8Hossain M A, Jia X, Picketing M. Subspace detectionusing a mutual information measure for hyperspectral im- age c]tassiication [ J ]. IEEE on Geoscience and Remote Sensing Letters ,2014,2 ( 11 ) :424 - 428. 被引量:1
  • 9Sun Y, Li J. Iterative Relief for feature weighting[ C]// Proceedings of the 23rd International Conference on Ma- chine Learning, 2006. 被引量:1
  • 10刘帅,李士进,冯钧.多特征融合的遥感图像分类[J].数据采集与处理,2014,29(1):108-115. 被引量:13

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