Memory multi-point crossover gravitational search-based feature selection method

Abstract

The invention discloses a memory multi-point crossover gravitational search-based feature selection method. Each particle is set as the alternative solution of an optimal feature subset; the quality of the alternative solutions are evaluated through a band subset evaluation function; particles are guided to carry out information exchange, and fast convergence can be completed; and solutions corresponding to particles with best quality are optimal spectral feature subsets. According to the method, in order to improve the adaptability of the algorithm, a population evolutionary degree-based information exchange mechanism is put forwards based on a gravitational search algorithm: in an exploration stage, the algorithm fully learns from other particles in a population based on a multi-point crossover strategy so as to carry out extensive search; and in a development stage, the algorithm learns from the optimal experiences of the population and the algorithm itself in a centralized manner so as to ensure fast convergence. With the memory multi-point crossover gravitational search-based feature selection method of the invention adopted, an optimal spectral feature subset which has few bands and can obtain stable classification results can be selected out, and therefore, problems such as complex calculation and low classification accuracy which are caused by high redundancy of hyper-spectral remote sensing image data can be solved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a feature selection method based on memory multi-point cross-gravity search. Background technique [0002] With the development of sensor technology, hyperspectral remote sensing images began to play an important role in environmental monitoring, land and resources survey and evaluation, urban planning and other fields after the 1980s. In this process, the classification accuracy of remote sensing images directly determines the effectiveness of its application. Although the rich spectral features of hyperspectral remote sensing images can more accurately describe the ground cover information, such massive spectral information usually has high redundancy, which will cause the Hughes phenomenon in the classification process and consume a lot of storage and computing space. Therefore, how to identify the most effective small number of bands from the high-di...

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Problems solved by technology

[0007] Aiming at the demand for remote sensing image feature selection and the shortcomings of the gravitational search algorithm, the purpose of the present invention is to provide a feature selection method based on memory multi-point cross-gravity search, through self-adaptive balance exploration and development optimization feature subset evaluation function , and mutate the global historical optimum to effectively improve the problems of insufficient development ability, lack of memory, and premature convergence of the gravitational search algorithm, and finally obtain the optimal spectral feature subset with a small number of bands and stable classification results

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