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21.
Outlier separability analysis is a fundamental component of modern geodetic measurement analysis, positioning, navigation, and many other applications. The current theory of outlier separability is based on using two alternative hypotheses—an assumption that may not necessarily be valid. In this paper, the current theory of outlier separability is statistically analysed and then extended to the general case, where there are multiple alternative hypotheses. Taking into consideration the complexity of the critical region and the probability density function of the outlier test, the bounds of the associated statistical decision probabilities are then developed. With this theory, the probabilities of committing type I, II, and III errors can be controlled so that the probability of successful identification of an outlier can be guaranteed when performing data snooping. The theoretical findings are then demonstrated using a simulated GPS point positioning example. Detailed analysis shows that the larger the correlation coefficient, between the outlier statistics, the smaller the probability of committing a type II error and the greater the probability of committing a type III error. When the correlation coefficient is greater than 0.8, there is a far greater chance of committing a type III error than committing a type II error. In addition, to guarantee successful identification of an outlier with a set probability, the minimal detectable size of the outlier (often called the Minimal Detectable Bias or MDB) should dramatically increase with the correlation coefficient. 相似文献
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运用高分辨率卫星遥感影像立体像对,利用少量的外业控制点,进行l:10000比例尺地形图立体测图精度试验,获取分辨率更高的彩色影像。 相似文献
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Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multi-temporal ENVISAT ASAR data with different
polarization are used to classify the banana fields in this paper. Principal component analysis (PCA) was applied for six
pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is
high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization
data provide more information, and the VV and VH backscatter of banana show different characters with other land covers. Based
on the analysis of the radar signature of banana fields and other land covers and the 1st component, banana fields are classified
using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study
area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes
the extraction of banana fields easier.
Supported by the Program for New Century Excellent Talents in University (NCET-05-0573), Fujian Science and Technology Project
(No.2006I0018), the Science Project of the Education Department of Fujian Province(No. 2006F5022). 相似文献
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