Classified extraction method of tobacco field remote sensing data

Abstract

The present invention provides a classified extraction method of tobacco field remote sensing data. Based on the monthly data set of a land resource satellite, the sequence data of a normalized difference vegetation index (NDVI), a normalized difference water index (NDWI) and a modified soil adjusted vegetation index (MSAVI) is obtained through band operation, thus the mathematical method operations of a mean square deviation, a half square deviation and a mean value are carried out, an indicator factor with a clear physical meaning and phenological information is obtained, a multi-dimensional feature space classification data set is constructed, and a support vector machine (SVM) is operated to automatically extract the farmland type of a paddy field and a dry land in a research area. The method has a certain practical significance for the rational use of the agricultural resources of the area, and a certain reference is provided for making the policies and plans of future agricultural development in the area by related administrative departments.

Description

technical field [0001] The invention relates to the field of tobacco field geographic information analysis methods, and more specifically, to a method for classifying and extracting tobacco field remote sensing data. Background technique [0002] At present, remote sensing classification and extraction of tobacco fields focus on research on the scale of medium and high resolution remote sensing images. There are mainly two methods for farmland remote sensing extraction: 1) manual visual interpretation; 2) computer automatic classification and extraction. The former has higher precision in tobacco field extraction, but has a long working cycle and high cost; the latter has faster land type information extraction and lower cost, but in areas with complex land cover types and large human activities, the results of automatic classification extraction There are still many omissions and misclassifications. In addition, during the peak growth period, the surface vegetation has si...

AI Technical Summary

Problems solved by technology

The former has higher precision in tobacco field extraction, but has a long working cycle and high cost; the latter has faster land type information extraction and lower cost, but in areas with complex land cover types and large human activities, the results of automatic classification extraction There are still many omissions and misclassifications

In addition, during the peak growth period, the surface vegetation has similar satellite image pixel DN values, similar ground object image texture features, and complex land cover types; these increase the difficulty of remote sensing classification and extraction of paddy fields and dry land in tobacco field areas

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