Hyper-spectral remote sensing image classifying method based on AdaBoost

Abstract

The invention discloses a hyper-spectral remote sensing image classifying method based on AdaBoost. A traditional mode identification method cannot meet the requirements of carrying out high-efficiency and high-precision classification on hyper-spectral data with high data dimensions and great data quantity; and although a neural network and a support vector machine can effectively classify remote sensing data, an ideal selection method of parameters does not exist. The hyper-spectral remote sensing image classifying method based on the AdaBoost comprises the following steps of: pre-processing the hyper-spectral data to remove abnormal wave bands influenced by factors including atmosphere absorption and the like; then utilizing MNF (Minimum Noise Fraction) conversion to carry out wave band preferential selection to achieve the aims of optimizing data, removing noises and reducing dimensions of the data; then, dividing a training sample and a test sample; selecting a decision stump as a weak classifier and utilizing an AdaBoost algorithm to train the weak classifier to obtain a strong classifier; selecting suitable iterations; and finally, utilizing a one-to-one method to establish a plurality of the classifiers. According to the hyper-spectral remote sensing image classifying method based on the AdaBoost, the convergence rate is enhanced and the classification performance of a hyper-spectral image is improved.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral data processing methods and applications, and relates to a hyperspectral remote sensing image classification method based on AdaBoost, which is suitable for the theoretical method and application technology research of hyperspectral data supervised classification. Background technique [0002] The spectral features of hyperspectral images have obvious high-dimensional features, and there is a strong correlation between features. It is inefficient to directly apply the original band for analysis. When traditional multispectral image classification methods are used to classify hyperspectral images, the phenomenon of "curse of dimensionality" will be encountered, and the amount of calculation increases with the number of bands to the fourth power. In order to better solve the problem of hyperspectral remote sensing image classification, Hughes phenomenon must be overcome. Reducing the data dim...

AI Technical Summary

Problems solved by technology

Traditional pattern recognition methods cannot meet the high-efficiency and high-precision classification of hyperspectral data with high data dimensionality and large data volume. Although neural networks and support vector machines can effectively classify remote sensing data, they require complex parameter search. Optimal settings

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