Hyperspectral image wave band selection method based on quantum-behaved particle swarm optimization algorithm

Abstract

The invention discloses a hyperspectral image wave band selection method based on the quantum-behaved particle swarm optimization algorithm to mainly solve the problems that in the prior art, searching capacity is low and classification accuracy is not high. The hyperspectral image wave band selection method includes the steps of firstly, inputting hyperspectral gray level images, and setting up a training set through samples with labels; secondly, initiating position vectors, code vectors, fitness values and local optimal information of particles and global optimal information of population; thirdly, renewing the position vectors and the code vectors of the particles; fourthly, calculating the fitness values of the particles according to the renewed code vectors; fifthly, renewing the local optimal information of the particles and the global optimal information of the population; sixthly, judging whether iteration is stopped or not, outputting the optimal wave bands corresponding to the global optimal information if the stopping conditions are satisfied, and executing the third step if the stopping conditions are not satisfied. By means of the hyperspectral image wave band selection method, effectiveness of wave band selection is improved, the optimal wave bands can be selected out as less as possible in a self-adaption mode on the premise that classification accuracy is ensured, and the hyperspectral image wave band selection method can be used for preprocessing the hyperspectral images before classification.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a method for selecting a band of a hyperspectral image. This method can be used to adaptively select a subset of bands with higher classification accuracy and fewer bands from multiple bands of hyperspectral images before hyperspectral image classification. Background technique [0002] With the development of hyperspectral remote sensing imaging technology, hyperspectral images have been widely used in agriculture, geology, coastal and inland water environment, atmospheric research, global environmental research and other fields. The hyperspectral imager can obtain almost continuous ground object spectral images in multi-band and narrow spacing, which makes hyperspectral images have higher spatial and spectral resolutions than traditional remote sensing images, and contains rich spatial, Classification information of ground features such as radiation and spectrum. However, ther...

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Problems solved by technology

However, there are still many shortcomings in the PSO algorithm, such as theoretically unable

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