Remote-sensing-image-based parallelization method of regression model of characteristic function space filter value

Abstract

The invention discloses a remote-sensing-image-based parallelization method of a regression model of a characteristic function space filter value. On the basis of the influence on a model by variable space autocorrelation during a remote sensing image data regression modeling process, a characteristic function spatial filtering method is employed; block division is carried out on an image and distributed calculation is carried out by using a constructed parallel computing cluster; and then a block division calculation result is returned to a main node for gathering. On the basis of comparison of regression model fitting evaluation parameters MSE, RMSE, R<2>, Adj.R<2> that are obtained by serial and parallel processing and a parallel speed-up ratio S, elimination of the spatial automation influence in the spatial statistic regression modeling by the remote-sensing-image-based characteristic function space filter value parallelization method can be demonstrated; and the computing efficiency can be improved effectively.

Description

technical field [0001] The invention belongs to the technical field of spatial statistical analysis service application, and in particular relates to a method for parallelizing a regression model of a feature function space filter value based on a remote sensing image. Background technique [0002] Spatial statistics is an important science to study the distribution, interrelationships and changing laws of geographical spatial objects and phenomena. As an important branch of spatial analysis, the development of spatial statistical analysis research provides a strong mathematical foundation and theoretical support for spatial data analysis. Remote sensing satellites can not only quickly and easily obtain the latest data, shorten the acquisition time of surface data, reduce the cost of data acquisition, but also have higher data accuracy, and have become an important data source for spatial statistics. Traditional spatial statistical methods usually use regional statistical da...

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Problems solved by technology

[0016] In order to solve the problems that the variable spatial autocorrelation affects the accuracy of the regression model in the spatial statistical regression analysis based on remote sensing images, and the large amount of data leads to insufficient computing power, the present invention provides a feature function spatial filter value based on remote sensing image data Parallelization Methods for Regression Models

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