Skip to main content
padlock icon - secure page this page is secure

A knowledge-based similarity classifier to stratify sample units to improve the estimation precision

Buy Article:

$61.00 + tax (Refund Policy)

This paper presents a comprehensive knowledge-based similarity classifier that uses remote sensing images and other auxiliary data to map spatial heterogeneity, for stratifying sample units distributed at the geographical landscape in order to improve the precision of the estimate of interest. Our method emphasises the decrease of bias so as to produce the high-quality stratifying frame. For this purpose, the method takes some necessary measures such as use of auxiliary variables including spectral bands, physical and socioeconomic data to help cluster analysis, correlation analysis between auxiliary variables and the goal variable to remove irrelevant data and consideration of spatial correlation in cluster analysis through the density-based unsupervised learning etc. Furthermore, considering the time-consuming characteristic of clustering huge spatiotemporal datasets, the method uses non-parametric supervised learning to induce rules for clustered classes. The rules could be efficiently used to group pixels into different classes of similarity. Then in the method, the pixel-level similarity image was vectorised into polygons with different group labels, thus producing the vector map of geospatial heterogeneity as an easy-to-use stratification frame. Last, to have an accurate estimation of the goal variable, our method re-divided sample units while the units covered by different strata and considered the effect of the sample size in the estimation algorithm. In the survey case of the cultivated land area, the proposed method achieves higher accuracy and a better coefficient of relative efficiency (RE) of stratification with its estimate closer to the observed value in comparison with other stratification strategies, e.g., k-means, SOM and those similar to eco-regions. Our method has potential practical merits as a good stratification strategy can increase the precision and considerably save the cost of sampling for many large regions, such as those in China, to be surveyed.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: 1: Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China,Department of Computing, The Hong Kong Polytechnic University, Hong Kong 2: Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China 3: Department of Computing, The Hong Kong Polytechnic University, Hong Kong

Publication date: January 1, 2009

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more