Remote sensing identification method for land cover and planting structure of finely-divided agricultural area

Abstract

The invention belongs to the field of agricultural remote sensing, and relates to a remote sensing identification method for land cover and planting structure of a fragmented agricultural area, which adopts a machine learning algorithm to extract land utilization, does not depend on actually measured sample points, and obtains high-precision agricultural area land utilization rasterized data after automatic training. According to the method, an agricultural area and a non-agricultural area are classified through a supervision algorithm support vector machine (SVM), and then crop types are further classified and discriminated by adopting a decision tree algorithm based on obtained agricultural area classification raster data. The method does not depend on actual measurement data, classification time and economic cost are greatly reduced, and negative effects of the quality of a sample training set of a traditional machine algorithm on a classification result are effectively avoided. And meanwhile, the land utilization mapping precision required by agricultural remote sensing application is met on the premise of ensuring high classification efficiency.

Description

A remote sensing identification method for land cover and planting structure in finely fragmented agricultural areas technical field The invention belongs to the field of agricultural remote sensing, relate to a kind of remote sensing sensing of land cover and planting structure in a finely fragmented agricultural area Another method, using machine learning algorithm to extract land use, does not rely on measured sample points, and obtains high-precision agriculture after automatic training District land use rasterized data. Background technique Accurate and timely acquisition of land cover information in agricultural areas is useful for monitoring agricultural conditions, adjusting agricultural structure, estimating yield and formulating food grains. Food policies, etc. are of great significance. Traditional field investigation methods are time-consuming and labor-intensive, and it is difficult to obtain large-scale regional land interest. use information. Remote ...

AI Technical Summary

Problems solved by technology

All in all, when the existing classification algorithms are applied to satellite remote sensing images, the calculation time of a single classification algorithm is long, the computer configuration requirements are high, and a large number of measured samples are required, which is time-consuming and laborious, so it is difficult to realize large-scale land use identification.

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