Multi-feature optimization and fusion method for crop planting structure extraction

A technology of planting structure and fusion method, applied in biological models, instruments, character and pattern recognition, etc., can solve the problems of ignoring the screening of classification features, ignoring differences in feature information, affecting classification accuracy, etc.

Pending Publication Date: 2020-11-17
CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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Problems solved by technology

[0004] In order to solve the problems existing in the prior art, the present invention provides a multi-feature optimization and fusion method for crop planting structure extraction, which solves the problem that the traditional method of obtaining crop planting structure information ignores the screening of classification feature quantities, and in the fusion process The difference between the amount of feature information is ignored, which increases the time complexity and affects the classification accuracy and other issues.

Method used

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  • Multi-feature optimization and fusion method for crop planting structure extraction
  • Multi-feature optimization and fusion method for crop planting structure extraction
  • Multi-feature optimization and fusion method for crop planting structure extraction

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Experimental program
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Effect test

Embodiment 1

[0094] Such as figure 1 As shown, a multi-feature optimization and fusion method for crop planting structure extraction includes the following steps:

[0095] S1: Taking the Shijin irrigation area in Hebei Province as an example, collect GF-1WFV satellite remote sensing data in September 2019 to achieve full coverage of the area and perform GF-1 preprocessing. At the same time, collect the main crop types and phenological changes in the research area. Through The field measurement completed the collection of sample points in the research area;

[0096] S2: According to the preprocessed GF-1 satellite remote sensing image data, calculate the vegetation index, texture features and spectral features in the study area;

[0097] S3: Calculate the expression of spectral information, vegetation index and texture features of different crop samples in GF-1 images, count the mean and variance of each feature, and calculate the distinguishability of different crop samples on each featur...

Embodiment 2

[0175] Taking two counties in Shijiazhuang as the research object, multi-feature screening and fusion are carried out according to the GF-1WFV data in May 2019, and finally the identification of crop planting structure is realized, such as Figure 9 shown. It can be seen from Table 3 that the image classification accuracy after screening and fusion reaches 86.16%. Compared with the traditional method of only using spectral information or texture information as classification features, the accuracy has been greatly improved. The interference between them reduces the time of training and classification, and improves the classification rate. Therefore, this method significantly improves the timeliness and accuracy of feature screening and fusion in practical applications.

[0176] Table 3 Comparison of crop extraction accuracy of different classification methods in May 2019

[0177]

[0178] During the implementation of this embodiment, the method proposed by the present inv...

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Abstract

The invention discloses a multi-feature optimization and fusion method for crop planting structure extraction, and the method comprises the steps: collecting a time sequence satellite remote sensing data set which is not greater than the monthly scale, and completing the pre-obtaining of sample data in a research region; describing spectral and texture characteristics of various crops; calculatingexpressions of different samples on spectral information, vegetation indexes, texture characteristic quantities and the like, counting mean values and variances of the characteristic quantities, andcalculating distinguishable capabilities of the different samples on the characteristic quantities; establishing a multi-feature optimization formula, and determining feature quantities participatingin classification and proportions of the feature quantities in the classification process by utilizing the formula; constructing a new image; and performing fine identification on the crop type of theresearch area by utilizing a random forest classifier, generating a space-time distribution thematic map of the crops, and verifying the precision. According to the method, the problem that the timecomplexity and the computer running speed are increased due to the fact that screening of the classification characteristic quantity is ignored in a traditional remote sensing information extraction method is solved.

Description

technical field [0001] The invention relates to the field of remote sensing planting structure monitoring, in particular to a multi-feature optimization and fusion method for crop planting structure extraction. Background technique [0002] Crop planting structure refers to the compilation of information such as the spatial distribution of different types of crops in the region and the planting area of ​​each type, and finally presents the crop information intuitively in the form of maps. Planting structure information reflects people's allocation of land resources, is the basis for analyzing crop planting area and counting crop types, and is also the basis for rational adjustment and optimization of land resources. The traditional method of obtaining crop planting structure is to report layer by layer from bottom to top, which cannot provide accurate spatial distribution information of crop categories, and there is a certain lag in time. With the development of science and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/00
CPCG06N3/006G06V20/188G06V10/267G06F18/24G06F18/253
Inventor 王镕赵红莉段浩蒋云钟郝震杨雯娜李向龙王占玲
Owner CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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