Classification-oriented hyperspectral image band selection method

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,...

AI Technical Summary

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 search effect

Later, a new population intelligent search algorithm—the gray wolf algorithm was proposed. Compared with other optimization algorithms, it has the characteristics of less adjustment parameters, fast convergence speed, and high execution performance, but it still has slow convergence speed when solving multimodal functions. Easy to fall into the disadvantage of local extremum

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