Hyperspectral remote sensing classification method based on support vector machine under particle optimization

Abstract

The invention discloses a hyperspectral remote sensing classification method based on support vector machine under particle optimization. High-efficiency high-accuracy classification of hyperspectral data which are high in data dimension and large in data volume can not be met by existing methods, and no ideal selection method is provided for parameters of a support vector machine method. According to the method, hyperspectral data is preprocessed, abnormal wave bands are removed under the influence of factors such as atmospheric absorption, then a certain proportion of data of various types are selected at random to serve as training data, a Gauss radical basis function is selected to serve as a kernel function mode, a classifier based on the support vector machine is trained, a speed updating formula of changing weight is designed, a certain proportion of particle mutation is guaranteed, an optimal classifier parameter is selected and obtained according to a particle swarm optimization algorithm, a plurality of second classifiers are trained, and a type which wins most votes is selected to be a final predicted type of data points according to a voting mode. According to the method, parameter optimization convergence ability of the classifier is strengthened, and the classification performance of hyperspectral remote sensing images is improved.

Description

technical field [0001] The invention belongs to the field of information technology, relates to artificial intelligence and pattern recognition technology, in particular to a hyperspectral remote sensing classification method based on a support vector machine under particle optimization. Background technique [0002] Using hyperspectral remote sensing to classify ground features is an important part of many remote sensing applications. Hyperspectral remote sensing can greatly obtain characteristic spectral curves of surface objects, and has the characteristics of high spectral resolution, strong spectral continuity, and high correlation between adjacent spectral bands. The characteristics of high data dimension, large data volume, data uncertainty and small sample classification are the key and difficult points of hyperspectral remote sensing data classification. [0003] In terms of classification processing technology, the multi-spectral remote sensing processing method i...

AI Technical Summary

Problems solved by technology

However, in the classification process of hyperspectral images, as the spectral resolution increases, the number of identifiable categories increases. These ground object categories that cannot be distinguished under multispectral conditions generally have poor separability, making Traditional taxonomy may no longer be appropriate

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