Training method for multi-output land cover classification model, classification method, and device
US12154044B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 25, 2020 |
| Grant date | Nov 26, 2024 |
| Priority date | — |
| Expiry date | Sep 28, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/814
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A training method for multi-output land cover classification model and a classification method are provided. The training method includes: obtaining a training data; inputting the training data into an initial model based on deep belief nets for training to obtain a multi-output land cover classification model, wherein the initial model includes N level outputs, and the N level outputs include an output set at last network layer and (N−1) level output set at any (N−1) network layers from a first network layer to a penultimate network layer of the initial model; determining a total loss according to losses of the N level outputs; performing a backpropagation based on the total loss to adjust a parameter of the initial model, N being an integer greater than or equal to 2. The gradient is not easy to disappear during backpropagation of the model, which is beneficial to improve classification accuracy.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.