Classification-oriented hyperspectral image band selection method

A hyperspectral image and band selection technology, which is applied in the direction of instruments, character and pattern recognition, and calculation models, can solve the problems of too many genetic algorithm parameters, slow convergence speed of gravity search algorithm, and unsatisfactory global search effect, etc.

Pending Publication Date: 2021-07-30
DALIAN MARITIME UNIVERSITY
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Problems solved by technology

Among them, the genetic algorithm has many parameters, the execution is complex, and it is easy to fall into the local optimum, and the performance of the global search is not good.
The discovery rate of the firefly algorithm is low, the solution accuracy is not high, and the convergence speed is slow
Gravitational search algorithm has slow convergence speed and unsatisfactory global sea

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  • Classification-oriented hyperspectral image band selection method
  • Classification-oriented hyperspectral image band selection method
  • Classification-oriented hyperspectral image band selection method

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Embodiment Construction

[0041] In order to make the technical solutions and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the drawings in the embodiments of the present invention:

[0042] Such as figure 1 A classification-oriented hyperspectral image band selection method shown specifically includes the following steps:

[0043] S1: Determine the hyperspectral data set, find the trace of the ratio of the inter-class dispersion matrix to the intra-class dispersion matrix for each band of the data set, and arrange them in descending order;

[0044] S2: On the basis of the classic gray wolf algorithm, the linear decreasing convergence factor is improved to an adaptive nonlinear decreasing convergence factor, which corresponds to the large search space in the early stage and the need for fast search, which requires the convergence factor to drop quickly, and the fine searc...

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Abstract

The invention discloses a classification-oriented hyperspectral image wave band selection method, which comprises the following steps of solving traces of a ratio of an inter-class dispersion matrix to an intra-class dispersion matrix of each wave band of a hyperspectral image, and arranging the traces in a descending order; improving a linear decline convergence factor into a self-adaptive nonlinear decline convergence factor by adopting a grey wolf algorithm; reading the first half of the hyperspectral image waveband sequence, performing random arrangement, and taking the first half of the hyperspectral image waveband sequence as an initial population of an improved grey wolf algorithm; using the trace of the ratio of the inter-class dispersion matrix to the intra-class dispersion matrix of each population as an objective function of the improved grey wolf algorithm, searching the maximum value of the objective function, wherein an individual corresponding to the maximum value is the selected wave band combination. The method can effectively select the waveband subset suitable for classification, considers that the basic grey wolf algorithm is slow in convergence speed and easy to fall into a local extremum, combines the class separability criterion with the grey wolf algorithm, improves the convergence factor, and improves the search performance of the grey wolf algorithm.

Description

technical field [0001] The invention relates to the field of hyperspectral image band selection, in particular to a classification-oriented hyperspectral image band selection method. Background technique [0002] Hyperspectral images have rich spatial and spectral information and are widely used in many fields. However, while a large amount of spectral information enhances the ability to distinguish ground objects, the high correlation between bands increases the complexity of subsequent processing algorithms and produces the "Hughes" phenomenon. Dimensionality reduction is a common method to reduce the computational complexity of hyperspectral images and improve classification performance, and it is also the best method to solve the "curse of dimensionality" problem of hyperspectral images. Band selection is an important technique for dimensionality reduction of hyperspectral images. [0003] Many scholars have introduced global optimization algorithms for band selection,...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/00
CPCG06N3/006G06V20/194G06V20/13
Inventor 王玉磊朱晴雨王凤超于浩洋于纯妍宋梅萍张建祎
Owner DALIAN MARITIME UNIVERSITY
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