Method of enhancing abnormal area of ground-penetrating radar image based on hybrid-supervised learning
US12175633B1 · kind B1 · utility
Inventors
Key dates
| Filing date | Jul 3, 2024 |
| Grant date | Dec 24, 2024 |
| Priority date | — |
| Expiry date | Jul 3, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of enhancing an abnormal area of a ground-penetrating radar image based on hybrid-supervised learning includes the steps of: building a database including a real image set, a simulation image set and a simulation image label set; adopting a generative adversarial network; processing semi-supervised training and unsupervised training alternately to obtain a trained model, then inputting a real radar image with abnormal area that needs to be enhanced into the model and processing through the generative network to output an abnormal-area-enhanced image. The method overcomes the problems of differences in characteristics between simulated images and real images, and low utilization efficiency of real image information by unsupervised methods, and improves the utilization efficiency of the enhanced network for real image information, the saliency of abnormal areas on real images, and the generalization ability of the enhanced network, therefore effectively enhances the significance of abnormal areas in ground-penetrating radar images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.