SAR image classification method based on hierarchical sparse filtering convolutional neural network
A convolutional neural network and sparse filtering technology, applied in the field of image processing, can solve the problems of limited application, low classification accuracy, difficult to overcome the influence of noise, etc., to achieve the effect of overcoming coherent speckle noise, high classification accuracy, and stable classification results
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Example Embodiment
[0032] Reference figure 1 The implementation steps of the present invention are as follows.
[0033] Step 1: Divide the SAR image database sample set into a training data set x and a test sample set y.
[0034] First, from each sample set containing 6 types of SAR image database sample sets, 1000 pictures with a size of 256×256 are taken, and then 200 pictures are randomly selected from each type of pictures to form the training set x, and the rest are used as the test set y.
[0035] Step 2: Randomly extract training image blocks of m block size d×d from the training data set x, and perform global contrast normalization to form a training image block set
[0036] Step 3: Use the training image patch set X to train the first layer of sparse dictionary.
[0037] 3a) Express the feature matrix of the training image block set X as:
[0038] F = ( X D ) 2 + ϵ ,
[0039] among them Represents a dictionary, N represents the number of features of each image bl...
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