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作 者:林剑[1,2] 谭勇鸿[2] 阳岳龙[2] 彭顺喜[3] 刘建勋[1]
机构地区:[1]湖南科技大学知识网格实验室,湖南湘潭411201 [2]湖南科技大学地信系,湖南湘潭411201 [3]中南大学信息物理工程学院,湖南长沙410083
出 处:《光谱学与光谱分析》2009年第2期436-440,共5页Spectroscopy and Spectral Analysis
基 金:国家"863"项目(2006AA04Z152);国家自然科学基金项目(60673119);湖南省自然科学基金项目(07JJ5066);湖南省教育基金项目(06c17)资助
摘 要:研究遥感图像多光谱特征分布是设计分类算法的关键。针对土地利用/覆盖类别由多种地物组成,难以求取多元分布模型分析类别多光谱特征的问题,提出了一种遥感图像土地利用/覆盖多光谱特征分布空间距离分析方法,以样品均值矢量作为类别中心,用最大最小法聚类形成类别多聚类中心,计算各聚类中心与类别中心的空间距离,以距离为横坐标、聚类中心含有的像元数占样品总数的百分比为纵坐标作图,形成内类、类间空间距离分布曲线分析遥感图像土地利用/覆盖类别多光谱特征。分类实验结果与空间距离分析结论吻合,说明空间距离分析方法可行。该方法利用空间距离把多维光谱分布转化为一维距离分布,计算简单,聚类门限值易于确定,多光谱分布特征清晰,较好地解决了多光谱特征的多元分布问题。Aiming at the problem that a convenient multivariate statistical model is in general not available for the multi-spectrum feature of land use/cover(LUC)class in remote sensing (RS) image, because the class is made of multiple covered species, a spatial-distanee analysis approach of multi-spectrum feature distribution for RS image LUC is present, with the mean vector of samples as LUC class center, with max-min clustering algorithm forming the class multi-clustering-centers, the spatial-distances from the class center to these multi-clustering-centers were calculated. With the distance as abscissa and the percentage of the clustering-center pixeis to the whole sample pixels as ordinate, the intra- and inter-classes distance distribution charts were constructed to analyze the multi-spectrum feature distribution of RS image LUC. The results of these samples classification tally with the conclusions of spatial distance analysis, indicating that this approach is feasible. In this approach the multi-dimensional spectrum information is turned into one dimensional distance information, the spatial-distance calculation and clustering threshold confirmation are realized easily, and the multi-spectrum feature of LUC class is clear, so it is a better approach to solving the multivariate distributing problem of multi-spectrum feature.
分 类 号:TP751.1[自动化与计算机技术—检测技术与自动化装置]
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