High-resolution remote sensing image classifying method based on fusion of multiple classifiers

Abstract

The invention discloses a high-resolution remote sensing image classifying method based on fusion of multiple classifiers. The high-resolution remote sensing image classifying method comprises the following steps that first, a training sample set is selected in an area of interest; second, the multiple classifiers are used for classifying remote sensing images; then, areas with the ground feature category classifying precision being lower than a threshold value a are classified again by using a voting method based on priori knowledge; at last, areas with the ground feature category classifying precision being lower than a threshold value b are classified by using a fuzzy decision template method, and finally the classified result of the target images is obtained. According to the high-resolution remote sensing image classifying method based on fusion of the multiple classifiers, the advantages of a single classifier are concentrated furthest, the disadvantages of the single classifier are restrained, the influences of 'same object with different spectrums ' and 'different objects with the same spectrum' on the classifying precision are lowered, and the precision of high-resolution remote sensing image classifying is improved.

Description

technical field [0001] The invention relates to a high-resolution remote sensing image classification method based on multi-classifier fusion, in particular to a high-resolution remote sensing image classification method based on multi-classifier fusion based on multi-level control, and belongs to the technical field of sensing image processing. Background technique [0002] Classification of remote sensing images is to use computers to analyze the spectral information and spatial information of various ground objects in remote sensing images, select features, and use certain means to divide the feature space into non-overlapping subspaces, and then classify each image in the image. Elements are assigned to each subspace. [0003] Compared with medium and low-resolution remote sensing images, high-resolution remote sensing images can more clearly express the spatial structure and surface texture characteristics of ground objects, and can distinguish the finer composition of ...

AI Technical Summary

Problems solved by technology

[0008] Purpose of the invention: In view of the problems existing in the prior art, the present invention provides a high-resolution remote sensing image classification method based on multi-classifier fusion, which adopts multi-classification combined with voting rules, prior rules, and fuzzy integral fusion rules Classifier combination rules, using fuzzy integral fusion algorithm to solve the problem of distinguishing difficult categories of a single classifier, for category samples that cannot be learned correctly, it can be learned intensively, and the learning results are output

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