Hyperspectral image waveband selecting method

Abstract

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a hyperspectral image waveband selecting method based on mimicry physics optimization algorithm. The method comprises the following steps of: (1) dividing subspaces; (2) generating initial population and initial speed; (3) obtaining an optimal fitness value and a worst fitness value; (4) obtaining the mass of individuals of the population, virtual acting force of other individuals on the individual and the resultant force of the acting force; (5) updating individual movement speed and position; (6) updating the optimal fitness value and the worst fitness value; and (7) judging whether the iterations t meet the condition that t is more than tmax; if so, stopping circulation; and if not, adding 1 to the iterations and continuing to execute steps (4)-(7).

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a hyperspectral image band selection method based on a mimic physics optimization algorithm, and belongs to the technical field of remote sensing image processing. Background technique [0002] With the development of remote sensing technology and imaging spectrometers, hyperspectral images have been more and more widely used. Since the hyperspectral image obtains almost continuous feature spectrum information, it has the incomparable feature recognition ability of other remote sensing images, but the hyperspectral image has a large number of bands and a large amount of data. Waiting for processing has brought great difficulties. For example, information redundancy is high, data storage requires a large space, processing time is long, and it is prone to the phenomenon of "dimensional curse". Therefore, it is very urgent and important to reduce the dimens...

AI Technical Summary

Problems solved by technology

Among these optimization algorithms, the genetic algorithm is a probabilistic search optimization algorithm, which can better solve the problem that the number of band combinations is large and difficult to traverse in the band selection process, but its convergence speed problem is still not satisfactorily solved, that is, Cannot converge efficiently in a finite time

The ant colony algorithm is a search algorithm of swarm intelligence, which can search for band combinations with better performance, but it generally requires a long search time, and is prone to stagnation, resulting in premature

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

In short, the existing algorithms cannot take into account both search efficiency and elaboration efficiency

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