Polarimetric SAR image terrain classification method based on self-supervised representation learning
A technology for classification and characterization of ground objects, applied in the field of image processing, can solve the problems of poor robustness, low classification accuracy of classifiers, and a large number of other problems, and achieve the effects of enhancing robustness, improving classification accuracy, and reducing demand.
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Example Embodiment
[0032] The implementation scheme of this example is to select two modal feature representations of polarimetric SAR data, and use the designed loss function and deep convolutional network framework to perform self-supervised representation learning features without using label information. The training is extracted, the deep convolutional network classifier is initialized with the learned parameters, and then the classifier is fine-tuned with the labeled training samples, and finally the test samples are classified.
[0033] refer to figure 1 , the specific implementation steps of this example are as follows:
[0034] Step 1. Divide the training set and the test set.
[0035] Obtain polarimetric SAR image data from different satellites, select an image sub-block from the image data as the data set S, and randomly select 5% of the unlabeled pixel data from the data set as the training set S for self-supervised representation learning 1 , randomly select 1% of the pixel data wit...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap