High-precision farmland vegetation information extraction method

Abstract

The invention discloses a high-precision farmland vegetation information extraction method, and relates to the technical field of agricultural remote sensing, and the technical scheme is characterized in that the method comprises the following steps: S1, collecting an original farmland image; S2, performing flat field correction processing on the farmland original image; S3, importing the image data after the flat field correction processing into Pix4Dmapper or ENVI software, and carrying out the image splicing and cutting; S4, labeling the image data, and formulating a neural network data set through a preprocessing mode of data enhancement and image cutting; S5, constructing and training a Unet neural network model; S6, saving the model, inputting a farmland image to be identified into the saved model, and obtaining an extracted farmland vegetation texture and spatial distribution information result. According to the method, the texture and spatial distribution information of the crops can be quickly and accurately obtained from the remote sensing image of the farmland, the problems of complex manual screening features and low recognition precision existing in satellite remote sensing interpretation of the farmland image are solved, and a reference method is provided for remote sensing interpretation of farmland crop information by an unmanned aerial vehicle.

Description

technical field [0001] The invention relates to the technical field of agricultural remote sensing, more specifically, it relates to a method for extracting farmland vegetation information with high precision. Background technique [0002] With the development of information technology and industry, remote sensing information has been widely used in the field of agriculture, such as crop classification, disaster prediction, cultivated land monitoring, yield prediction, etc. Traditional information acquisition and research on farmland crops mainly include manual feature selection, machine learning methods such as support vector machines, but they have certain limitations. In recent years, deep learning semantic segmentation has made great breakthroughs in the field of image classification, and has obvious advantages over manual feature classification methods. Scientific analysis and decision-making of agricultural production through the collection of farmland data by drones ...

AI Technical Summary

Problems solved by technology

In fact, due to the influence of factors such as the inhomogeneity of the light source, the inconsistency of the pixel response, the dark current bias, or some factors in the optical path that affect imaging (such as dust on the surface of the detector), the UAV’s For a target with uniform grayscale, the camera may output an image with uneven intensity, which is not conducive to the extraction and monitoring of target features in subsequent image processing, making the accuracy of farmland vegetation information extraction low

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