Multi-kernel support vector machine classification method for remote sensing images

Abstract

The invention discloses a multi-kernel support vector machine classification method for remote sensing images, and belongs to a support vector machine classification method for the remote sensing images. The method comprises the following steps of: performing principal component transform on original data; taking first four principal components to represent spectral information, performing wavelet texture feature extraction on the first principal component, and combining the spectral feature and spacial feature by adopting two independent radial basis functions; and finally performing classification by utilizing a multi-kernel support vector machine method. The wavelet texture feature and the spectral feature are combined thorough a plurality of basis functions, so the spectral feature extracted by principal component analysis is fully utilized, the wavelet texture feature is fused, the support vector machine is optimized, and the limitation that the traditional method separately adopts the spectral feature for classification is overcome; therefore, the classification accuracy is effectively improved. The method has the main advantage of improving the classification accuracy by combining the spectral information and the spacial information through the plurality of basis functions.

Description

technical field

[0001] The invention relates to a support vector machine classification method for remote sensing images, in particular to a multi-core support vector machine classification method for remote sensing images. Background technique

[0002] The support vector machine algorithm (support vector machine, SVM) has better performance than the traditional classification method in hyperspectral data classification, and the remote sensing image classifier designed based on the support vector machine algorithm has achieved good results in practical applications. However, in the classification process, the classifier only uses spectral data to learn and often cannot achieve good classification results. The usual process of using the support vector machine classification algorithm for remote sensing image classification is: through training samples of known categories in remote sensing images to support The vector machine classifier is trained to establish a classification...

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