主成分分析在遥感影像数据中的实例应用  

Practical Application of Principal Component Analysis in Remote Sensing Image Processing

作　　者：密长林[1] 马爱功[1] 张晓东 孙景广[1] 杨雪莲[1] 

机构地区：[1]临沂市国土资源局,山东临沂276000 [2]宁夏回族自治区地质调查院,宁夏银川750000

出　　处：《山东国土资源》2013年第7期69-71,76,共4页Shandong Land and Resources

摘　　要：主成分分析(Principal Component Analysis)是根据变量之间的相互关系,尽可能不丢失信息地用几个综合性指标表示多个变量的方法。在多(高)光谱图像中,由于各波段的数据间具有相关性,因此包含许多冗余信息。通过主成分分析法可以把遥感图像中所含的大部分信息用少数波段表示出来,这样就可以几乎不丢失数据但可以减少数据量,消除冗余信息。在遥感数据处理时用主成分分析法作数据分析前的预处理,以达到数据压缩和图像增强的效果,更加有利于影像信息提取。文章对主成分分析在遥感图像处理中的实际应用进行了实例示范应用研究。Principal component analysis is a method to express several multi-variable factors without losing information as far as possible based on relations of variables.In multi-spectral images and hyper-spectrum images,there is much redundant information because of relaion of the data in different bands,including many redundant information.Through principal component analysis,most information of remote sensing images can be expressed with fewer bands.Thus,it can not only reduce data size,but also eliminate redundant information.Principal component analysis method is always used to conduct data pre-processing in order to depress data and enhance images.In this paper,practical application of principal component analysis in remote sensing image processing has been studied.

关 键 词：主成分分析 特征值 遥感 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]