Target identification method of remote sensing image of artificial immune network based on self-adaptive PSO (Particle Swarm Optimization)

Abstract

The invention discloses a target identification method of a remote sensing image of an artificial immune network based on a self-adaptive PSO(Particle Swarm Optimization), mainly overcoming the disadvantages of low target identification precision and low convergence speed in the traditional method. The identification method comprises the following steps of: firstly, extracting 7 invariant moment characteristics of an image target and carrying out normalization treatment on the characteristic data; secondly, setting running parameters, selecting a training sample and initializing an immune network and immune cells; thirdly, calculating the affinity degree of the immune cells and cloning the immune cells; fourthly, executing hyper-mutation operation based on the self-adaptive PSO; fifthly, selecting an immune cell with highest affinity degree and adding the immune cell into the immune network; sixthly, carrying out network inhibition operation; seventhly, judging a stop condition, turning to the eighth step eight if the condition is satisfied, and otherwise, and otherwise jumping to the third step; and eighthly, inputting characteristic values of the remote sensing images which are not used as training samples into the immune network, and judging a category attribute value of each image by the immune network. The method has the advantages of high target identification accuracy and stable target identification performance and can be used for solving the problem of target identification of a remote sensing image set.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to a remote sensing image target recognition method, which can be used for detection and recognition of remote sensing image sets. Background technique [0002] Remote sensing and its information processing technology have played a key role in high-tech countermeasures such as target identification and positioning, real-time tracking, early warning, and electronic countermeasures. Remote sensing image classification and recognition is a branch of remote sensing image information processing technology. The classification and recognition process is the process of classifying image pixels into a certain class. The concept of "class" here can be a certain feature, landform or Different states of the same feature, once a target is classified into a certain category, its specific properties can be analyzed more accurately and conveniently. [0003] The use of remote sensing images ...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the selection of the initial population has a great influence on the recognition results, and the global search ability of this genetic algorithm is limited, resulting in unsatisfactory recognition results.

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