Hyperspectral image waveband selection method based on quantum evolution particle swarm optimization algorithm

Abstract

The invention relates to a hyperspectral image waveband selection method based on a quantum evolution particle swarm optimization algorithm, and belongs to the field of image processing. The hyperspectral image waveband selection method comprises the following steps: inputting a hyperspectral image of a waveband to be selected, and setting the scale, dimension and maximum iteration frequency of apopulation; mapping the position occupied by each particle from the unit space to the solution space of the optimization problem, and selecting the combination of the inter-class separability and theoptimal index as a fitness function; and introducing the variation probability into a quantum evolutionary particle swarm algorithm, classifying the output optimal waveband combination image by adopting a maximum likelihood method, calculating the overall classification precision, and calculating the average correlation between waveband combination wavebands adopted by the correlation. The hyperspectral image waveband selection method combines the quantum evolution particle swarm optimization algorithm with the particle swarm optimization algorithm, and can overcome the defect that local optimization is likely to happen, and the quantum evolution particle swarm optimization algorithm has the higher convergence speed, so that the operation time of the algorithm is shortened, and when waveband selection is carried out, the algorithm is more stable, and the classification precision is high, and the application prospect is wide.

Description

Technical field [0001] The invention relates to a hyperspectral image band selection method based on quantum evolution particle swarm algorithm, which belongs to the field of image processing. Background technique [0002] The band width of hyperspectral images is narrow and the spectral range is large. From this, it can be known that the correlation between the bands of hyperspectral images is very large, and the amount of information is large, and it is very difficult to select the bands. Hyperspectral image band selection is to select a combination of bands with large amount of information, small correlation and high resolution, that is, to search for bands that meet the corresponding conditions from hundreds of high-correlation and large-data-volume hyperspectral image band databases combination. [0003] With the development of intelligent swarm algorithms in recent years, many search algorithms have been applied to dimensionality reduction. For example, Zhao Dong and Zhao Gu...

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Problems solved by technology

[0003] In recent years, with the development of intelligent swarm algorithms, many search algorithms have been applied to dimensionality reduction. For example, Zhao Dong and Zhao Guangheng used genetic algorithms to select hyperspectral image bands; band selection based on genetic algorithms can better solve the problem of band selection. There are many band combinations and it is difficult to traverse the problem, but the problem of its convergence speed is still not satisfactorily solved

Zhou Shuang used the ant colony algorithm to reduce the dimensionality of hyperspectral images; the band selection of the ant colony algorithm can search for band combinations with better performance, but it generally takes a long time to search, and is prone to stagnation, resulting in premature

Huang Rui used the particle swarm optimization algorithm to reduce the features of hyperspectral remote sensing data. The particle swarm optimization algorithm is an optimization algorithm based on population iteration. Although its convergence speed is fast, its convergence accuracy is not high, and it is prone to premature

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