Intelligent extraction method of build-up area on the basis of nighttime light data

Abstract

The invention relates to an intelligent extraction method of a build-up area on the basis of nighttime light data. The intelligent extraction method comprises the following steps: adopting an adaptive particle swarm optimization algorithm to realize the optimal selection of the selection parameters of a VIIRS (Visible Infrared Imaging Radiometer Suite) nighttime light image sample and a MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index image sample; adopting a region growing algorithm based on SVM (Support Vector Machine) classification to finish SVM model training, and adopting a cross validation method to carry out precision validation on the model; and according to an optimized parameter, determining a city sample and a non-city sample, and adopting the region growing algorithm based on the SVM to extract a city built-area range. By use of the intelligent extraction method, from a sample selection source, the adaptive optimization of a sample selection parameter is carried out, and the SVM and the region growing algorithm are adopted to improve processing efficiency and precision for the nighttime light data to extract the nighttime light data.

Description

technical field [0001] The invention relates to an extraction method, in particular to an intelligent extraction method for built-up areas based on night light data. Background technique [0002] With the acceleration of urbanization, research on the spatial layout and spatial expansion of urbanized regions is becoming more and more important at the regional and even national scales. At present, the extraction of urban built-up areas mainly uses land use survey data, TM / ETM or MODIS image interpretation data, but the processing efficiency is slow, and there is a certain difference between the extracted built-up area results and the real development level of the city. The extraction of city limits from remote sensing data has attracted more and more attention. Currently, the commonly used methods focus on the threshold method. However, the accuracy of using the threshold method to extract the range of built-up areas from nighttime light remote sensing data is limited, and the...

AI Technical Summary

Problems solved by technology

At present, the extraction of urban built-up areas mainly uses land use survey data, TM / ETM or MODIS image interpretation data, but the processing efficiency is slow, and there is a certain difference between the extracted built-up area results and the real development level of the city. The extraction of city limits from remote sensing data has attracted more and more attention. Currently, the commonly used methods focus on the threshold method. However, the accuracy of using the threshold method to extract the range of built-up areas from nighttime light remote sensing data is limited, and the selection of the threshold is greatly affected by time and regional differences. , the same threshold cannot adapt to the extraction of built-up areas in different cities at different times

[0003] In recent years, some researchers have used automatic classification algorithms to extract the range of urban built-up areas by combining nighttime light data and vegetation index data. However, the introduction of multi-source data also leads to the complexity of classification algorithms. Information, constructing an intelligent optimization algorithm that can adapt to the city's own characteristics and time, and realize the intelligent extraction of urban built-up areas is a problem to be solved by existing technologies

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