Domain adaptive target recognition method based on deep knowledge integration

A technology of knowledge integration and target recognition, applied in the field of target recognition, it can solve the problems of model performance degradation, failure to explore different feature relationships, and inability to cope with changes in target domains, so as to improve robustness and overcome differences in data distribution.

Pending Publication Date: 2021-11-16
YANGTZE DELTA REGION INST (QUZHOU) UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

However, this method does not explore the relationship between different features, ignoring that there are often both public knowledge and unique knowledge between different features
Literature "J. Zhang, M. Xing and Y. Xie," FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features," in IEEE Transactions on Geoscience and Remote Sensing, vol.59, no.3 ,pp.2174-2187,March2021"proposed a decision-level knowledge integration method, splicing the results of different decision-making level

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  • Domain adaptive target recognition method based on deep knowledge integration
  • Domain adaptive target recognition method based on deep knowledge integration
  • Domain adaptive target recognition method based on deep knowledge integration

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Embodiment

[0081] This model is used to conduct experiments on the acquisition and recognition of ten types of targets in the MSTAR data set of moving and stationary targets in the United States. The sensor for collecting this data set is a high-resolution spotlight synthetic aperture radar, which works in the X-band and uses HH poles. The resolution is 0.3m×0.3m. Pre-processing is performed on the collected data, and a slice image with a pixel size of 128×128 including various targets is extracted from it. Most of the data are SAR slice images of stationary vehicles, including ten types of targets including BMP2, T72, BTR70, 2S1, BRDM2, BTR60, D7, T62, ZIL131, ZSU234 and T72. The sample data observed at an elevation angle of 17° is the source domain sample, and the sample data observed at an elevation angle of 15° is the target domain sample, which is recorded as the standard operating condition, including 10 types of targets, and the specific number of samples is shown in Table 1. The...

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Abstract

The invention belongs to the technical field of target recognition, and particularly relates to a domain adaptive target recognition method based on deep knowledge integration. According to the method, feature-level and decision-level deep knowledge integration is realized. A public mapping matrix and a specific mapping matrix are designed at a feature level to realize knowledge integration, and the robustness of target recognition performance is improved; and the public mapping matrix fully excavates public knowledge of heterogeneous features, and the specific mapping matrix retains specific knowledge of different features. Feature weights are designed at the decision level to quantify the importance degrees of different features, meanwhile, the feature weights are updated through online learning by means of target domain samples, the data distribution difference of different fields is overcome, and field self-adaptive target recognition is achieved. Therefore, the domain adaptive target recognition method based on deep knowledge integration provided by the invention is an intelligent domain adaptive target recognition method.

Description

technical field [0001] The invention belongs to the technical field of target recognition, and in particular relates to a field-adaptive target recognition method based on deep knowledge integration. Background technique [0002] Automatic target recognition technology can identify and classify targets based on sensor data, and plays an important role in military and civilian fields such as battlefield awareness and reconnaissance, terrain exploration, and automatic driving. With the continuous development of technology, target recognition methods with their own advantages have been proposed one after another. How to combine the advantages of different methods to improve the performance of target recognition has become one of the hot spots in the research of target recognition technology. [0003] Literature "Q.Yu, H.Hu, X.Geng, Y.Jiang and J.An,"High-Performance SARAutomatic Target Recognition Under Limited Data Condition Based on a DeepFeature Fusion Network,"in IEEE Acce...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/21G06F18/24G06F18/214
Inventor 郭贤生张玉坤段林甫陆浩然袁杨鹏黄健李林
Owner YANGTZE DELTA REGION INST (QUZHOU) UNIV OF ELECTRONIC SCI & TECH OF CHINA
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