Article(id=1242117693243981937, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242117680468131883, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1249315200000, receivedDateStr=2009-08-04, revisedDate=1257177600000, revisedDateStr=2009-11-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1258041600000, onlineDateStr=2009-11-13, pubDate=1258041600000, pubDateStr=2009-11-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1258041600000, onlineIssueDateStr=2009-11-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073535498, creator=sys-migrate, updateTime=1774073535498, updator=sys-migrate, issue=Issue{id=1242117680468131883, tenantId=1146029695717560320, journalId=1146031591421210625, year='2009', volume='27', issue='0921', pageStart='1', pageEnd='140', issueExtLink='null', onlineDate='null', pubDate='1258041600000', pubDateStr='2009-11-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774073532452, creator='sys-migrate', updateTime=1774073532452, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=91, endPage=94, ext={EN=ArticleExt(id=1242117695647318163, articleId=1242117693243981937, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=An Object-Oriented Optimal Scale Choice Method of High Spatial Resolution Remote Sensing Image, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=The traditional pixel-based information extraction and classification method is not suitable for processing high spatial resolution remote sensing images because it only focuses on spectral information and ignores information concerning texture, shape and structure related with adjacent pixels. The object-oriented information and classification extraction approach can solve this problem, with the basic unit being the image object, which enjoys good integrity and uniqueness in a multi-scale segmentation algorithm. The related experimental researches show that it is necessary to extract the region of interest in optimal scale images. In view of this, the RMAS method is based on analysis of the limitations of two optimal scale selecting methods, according to the best classification principle as "homogeneity in class, heterogeneity between classes". This method makes the heterogeneity in class the minimum and that between class the maximum when RMAS is the maximum, so the segmentation scale is optimal. According to the principle of the highest information extraction accuracy based on the optimal scale, the experiment has verified the feasibility of this method and the classification results are found to be better. It is shown that several local peaks appear in the curve of RMAS and that the optimal scale is relative and usually in a range of values. So, it is difficult to extract information using only one scale for the class with a large area. The optimal scale should be very carefully chosen according to specific application cases., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=zhang Jun, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=lzNHmPcKvQcKu9Rz5KWbqQ==, pdfFileSize=555027, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242117694800073162, articleId=1242117693243981937, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=一种面向对象的高分辨率影像最优分割尺度选择算法, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=近年来越来越多的高分辨率遥感卫星得到应用，传统方法已然不能满足高空间分辨率遥感影像的应用需求，面向对象的遥感影像处理方法应运而生。面向对象方法的基本处理单元是经过多尺度分割的具有较好的完整性和单一性的影像对象，相关研究表明不同目标有其最适宜的提取尺度。在分析两种最优尺度选择方法局限性的基础上，根据“类内同质性大，类间异质性大”的最佳分类原则，提出面向对象的RMAS方法。该方法的思想是，当对象RMAS值最大时，对象内部的异质性最小、对象外部的异质性最大，此时的分割尺度为类别提取的最优分割尺度。根据最优尺度下信息提取精度最高的原理，实验验证了该方法的可行性，且能获得较好的分类结果。分析还发现RMAS折线有时会出现多个局部峰值的情况，说明最优尺度是相对的，通常是一个数值范围，对于面积较大的类别使用一种尺度不易将信息准确提取出来，需要根据应用目标选择合适的最佳尺度。, authors=张俊¹,汪云甲²,李妍²,王行风², authorsList=张俊;汪云甲;李妍;王行风, authorCompany=1. 国家测绘局第三地形测量队，哈尔滨 1500812. 中国矿业大学环境与测绘学院，江苏徐州 221008, correspAuthors=null, authorNote=null, correspAuthorsNote=张俊, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=8lHyA6CEEUTcUYmmIl9YmA==, pdfFileSize=555027, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null)}, 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