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Training method, wheat recognition method and training system based on random forest model

A technology of random forest model and training method, applied in character and pattern recognition, computer parts, instruments, etc., can solve the problem of low efficiency of ground object recognition, and achieve the effect of alleviating low efficiency

Pending Publication Date: 2019-05-24
NORTH CHINA INST OF AEROSPACE ENG
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, the object of the present invention is to provide a training method based on a random forest model, a wheat identification method and a training system, to alleviate the technical problem of using remote sensing technology to identify low efficiency of ground features

Method used

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  • Training method, wheat recognition method and training system based on random forest model
  • Training method, wheat recognition method and training system based on random forest model
  • Training method, wheat recognition method and training system based on random forest model

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Embodiment 1

[0052] like figure 1 As shown, it is a flowchart of a training method based on a random forest model provided by an embodiment of the present invention. The method includes steps S101-S104, specifically as follows:

[0053] Step S101, obtaining a random forest model, wherein the random forest model includes multiple decision trees, the random forest (Random Forest, RF) model is a classification recognition model of wheat machine learning, and is used to classify according to whether the ground object is wheat, and can be Divided into wheat and non-wheat. The random forest model is composed of multiple decision trees, and the multiple decision trees are not related to each other. Decision tree (tree) is a tree-like structural form, which can be non-binary tree structure or binary tree structure. The random forest model in the embodiment of the present invention is a binary tree structure, and each decision tree is based on classification and regression (Classification And Regr...

Embodiment 2

[0079] A wheat identification method based on a random forest model, comprising:

[0080] Input the sample data into the tuned random forest model to get the prediction data of the sample data. Use MATLAB software to restore these forecast data and arrangement forms to the original text storage type, open it with remote sensing image processing software, and you can view the wheat distribution map. Carry out image processing operation to above-mentioned distribution map, image processing operation can comprise: Wheat distribution Figure II The binary image of wheat distribution is obtained by value processing, and the "noise" in the image is removed; the binary image of wheat distribution is converted into a vector image of wheat distribution, so as to eliminate the interference area, that is, the small area in the vector image of wheat distribution is deleted. Finally, a high-precision spatial distribution map of wheat was obtained.

Embodiment 3

[0082] like Figure 4 As shown, a structural frame diagram of a training system based on a random forest model provided by an embodiment of the present invention, including:

[0083] The random forest model obtaining module S401 is used to obtain a random forest model, wherein the random forest model includes multiple decision trees;

[0084] The training sample data acquisition module S402 is used to acquire a plurality of training sample data, the training sample data includes feature sample data and category data;

[0085] Prediction module S403, used to sequentially input multiple sample data into the random forest model to obtain multiple forecast data;

[0086] The accuracy calculation module S404 is used to calculate the accuracy of the random forest model according to multiple prediction data and multiple category data;

[0087] The optimization module S405 is configured to optimize the random forest model according to the accuracy to obtain an optimized random fores...

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Abstract

The invention provides a training method, a wheat recognition method and a training system based on a random forest model, and relates to the technical field of computer application technology, and the method comprises the steps: obtaining the random forest model which comprises a plurality of decision trees; obtaining a plurality of pieces of training sample data, wherein the training sample datacomprises category data and feature sample data; sequentially inputting the plurality of training sample data into the random forest model to obtain a plurality of pieces of prediction data; calculating the precision of the random forest model according to the prediction data and the category data; And optimizing the random forest model according to the precision to obtain an optimized random forest model, thereby alleviating the technical problem of low ground object recognition efficiency by using a remote sensing technology.

Description

technical field [0001] The invention relates to the technical field of computer application technology, in particular to a training method based on a random forest model, a wheat identification method and a training system. Background technique [0002] In recent years, the research on wheat extraction using remote sensing technology has made great progress. The first identification and extraction technology is image visual interpretation, which is widely used in the information extraction of various ground objects. Although there are many application scenarios of image visual interpretation, this method is inaccurate in positioning, poor in timeliness, poor in repeatability, and there are individual differences, so it is difficult to meet the growing needs of users. [0003] Aiming at the low efficiency of using remote sensing technology to identify ground objects, no effective solution has been proposed yet. Contents of the invention [0004] In view of this, the object...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCY02T10/40
Inventor 李旭青刘世盟金永涛李龙
Owner NORTH CHINA INST OF AEROSPACE ENG
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