Superpixel classification method based on semi-supervised K-SVD and multiscale sparse representation
US10691974B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 8, 2018 |
| Grant date | Jun 23, 2020 |
| Priority date | — |
| Expiry date | Jan 1, 2039 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02A40/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention discloses a superpixel classification method based on semi-supervised K-SVD and multiscale sparse representation. The method includes carrying out semi-supervised K-SVD dictionary learning on the training samples of a hyperspectral image; using the training samples and the overcomplete dictionary as the input to obtain the multiscale sparse solution of superpixels; and using the obtained sparse representation coefficient matrix and overcomplete dictionary to obtain the result of superpixel classification by residual method and superpixel voting mechanism. The proposing of the present invention is of great significance to solving the problem of salt and pepper noise and the problem of high dimension and small samples in the field of hyperspectral image classification, as well as the problem of how to effectively use space information in classification algorithm based on sparse representation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.