System and method for recognizing remote sensing image target based on migration network learning

Abstract

The invention discloses a system and a method for recognizing a remote sensing image target based on migration network learning, mainly solving the problems that the correct recognition rate for a remote sensing image with a label is relatively low when the number of data is less and the obtaining of the image label is difficult and needs high cost in the conventional methods. The whole system comprises an image characteristic extracting module, a migration network classifier learning system generating module and a migration network classifier learning system learning module, wherein the image characteristic extracting module is used for completing the characteristic extraction of the image; the migration network classifier learning system generating module is used for training input sample data by a network integrated learning algorithm introduced into migration learning to obtain a migration network classifier learning system; and the migration network classifier learning system learning module is used for completing the classification and the recognition of the characteristics of a new sample image. The invention has the advantage of the capability of utilizing other existing resources to improve the correct recognition rate of the remote sensing image target without collecting data again and can be used for the target recognition of the remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a remote sensing image target recognition system, which can be used for remote sensing image target recognition. Background technique [0002] In recent years, with the development of machine learning, the learning system based on a single classifier can no longer meet the needs of users, and integrated learning is called a research hotspot in machine learning. The integration technology uses multiple versions of the base learner to solve the same problem, which can significantly improve the generalization ability of the learning system, which requires each base classifier to be independent and differentiated, and has been applied to remote sensing image target recognition . [0003] In 2005, Wang Shijun and others introduced the Boosting algorithm into the classifier network, integrated the classifier network and the classifier, and proposed the network ens...

AI Technical Summary

Problems solved by technology

[0004] 1. When there is little labeled image data, the recognition accuracy will be low;

[0005] 2. If the accuracy of recognition is to be improved, the acquisition and collection of images and their labels is not only difficult but also expensive;

[0006] 3. It is not possible to use other existing available resources to improve the accuracy of recognition

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