Image classifying method and system for self-adaption weight fusion of multiple classifiers

Abstract

The invention relates to an image classifying method and system for self-adaption weight fusion of multiple classifiers. Multiple features capable of giving consideration to different kinds of objects are selected, and comprise spectral feature, textural feature, fractal dimension feature and topic feature; for different features, different classifiers are selected and then fused, compared with the single classifier, the fusion classifier has higher classifying precision and better classifying effect. Meanwhile, weights of component classifiers are self-adaptively designed according to classifying reliabilities of the component classifiers to the samples without being fused and decided by one weight which is fixed for all samples.

Description

technical field [0001] The invention belongs to the field of remote sensing image applications, in particular to a remote sensing image classification method and system for multi-classifier self-adaptive weight fusion. Background technique [0002] Remote sensing image classification is an important aspect of remote sensing interpretation research, and many remote sensing applications are directly related to image classification. Traditional remote sensing image classification techniques are mostly pixel-level methods based on probability and statistics models. Statistical classification methods are generally divided into two types: supervised classification methods and unsupervised classification methods. Supervised classification methods require certain prior knowledge, such as the probability distribution of samples and high-quality training samples. Because remote sensing images are usually complex, the data sample features have many dimensions, and data samples with mu...

AI Technical Summary

Problems solved by technology

Doing so brings at least three problems: all features are arranged into feature vectors, which will bring about the problem of "dimension disaster" and increase the complexity of calculation; for different types of features, their representation forms, physical meanings, and dimensions are different. Similarly, scale normalization is required to arrange them into feature vectors, and effective normalization of different features is very difficult; different features are simply combined together, and it is difficult to choose a suitable classifier

Its disadvantage is that when the number of training samples is small or the quality of training samples is not high, the weight of the classifier may not correctly reflect the

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