Winter wheat remote sensing recognition method capable of synthesizing key seasonal aspect characters and fuzzy classification technology

Abstract

The invention belongs to the remote sensing monitoring technical field, and particularly relates to a winter wheat remote sensing recognition method capable of comprehensively using key seasonal aspect characters and a fuzzy classification technology. The winter wheat remote sensing recognition method capable of comprehensively using the key seasonal aspect characters and the fuzzy classification technology includes steps: preprocessing data, preparing an abundance map under coarse resolution of a research area, obtaining membership degrees of pixel elements for winter wheat under a middle and high resolution scale; performing comprehensive judgment and the like. The winter wheat remote sensing recognition method capable of synthesizing the key seasonal aspect characters and the fuzzy classification technology synthesizes a method which is based on a seasonal aspect rhythm and uses time advantages of low resolution remote sensing with the fuzzy classification technology which uses spectrum information of middle and high resolution remote sensing, and thereby obtains a middle and high resolution identification result with definite space distribution, remedies respective defects of the method based on the seasonal aspect rhythm and the fuzzy classification technology, not only solves uncertain problems in the fuzzy classification technology when membership probabilities of the pixel elements for various types are comparative, but also solves the problem that an abundance map obtained by using the seasonal aspect characters can not show the definite space distribution of crops, and provides new monitoring and estimating means to remote sensing monitoring of the winter wheat.

Description

technical field [0001] The invention belongs to the technical field of remote sensing monitoring, and in particular relates to a remote sensing recognition method for winter wheat which comprehensively utilizes key seasonal characteristics and fuzzy classification technology. Background technique [0002] In the prior art, methods for identifying crop types based on remote sensing technology mainly include methods based on spectral information and methods based on seasonal rhythms. [0003] The method based on spectral information recognition is mainly used in medium and high-resolution remote sensing images. The principle is to use the statistical characteristics of pixel values ​​for classification and recognition. deviation. [0004] The recognition method based on seasonal rhythm features is mainly used for low spatial resolution remote sensing images that can form a time series. The principle is to use the difference in growth rhythm between crops and other vegetation,...

AI Technical Summary

Problems solved by technology

But the problem with this method is that when there are two or more classes with the highest degree of membership, there is obvious uncertainty even if the pixel belongs to the class with the highest degree of membership

[0007] In the existing technology, using remote sensing images to monitor the planting area of ​​winter wheat is a relatively fast and intuitive technical means, and there are many technical means for the interpretation of remote sensing images, but due to the inherent advantages and disadvantages of various technical means Therefore, it is often necessary to use one or more technologies together and make mutual corrections at the same time to obtain accurate monitoring data. However, in the existing technology, there is no correlation between the integrated key seasonal characteristics and fuzzy classification technology for remote sensing identification of winter wheat. to report

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