Polarization SAR image classification based on CNN and SVM
An image and classifier technology, applied in the field of image processing, can solve the problems of small number of features, underutilization of polarization information, arbitrary division of regions, etc., and achieve the effect of improving classification accuracy
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Example Embodiment
[0035] Example 1
[0036] The present invention is a polarization SAR image classification method based on CNN and SVM, see figure 1 , Polarization SAR image classification includes the following steps:
[0037] Step 1. Input the filtered polarized SAR image to be classified, see figure 2 , To obtain the polarization coherence matrix T, the polarization SAR image is a Dutch farmland map, mainly including rapeseed, sugar beet, potato, alfalfa, grassland, wheat, peas and other crops and a piece of bare land. Different colors in the picture represent different The category of this example is to classify this polarized SAR image, and the polarized SAR image to be classified is attached with a reference map for the distribution of ground features, see image 3 In the figure, some pixels are classified; the image filtering to be classified is mainly the polarization refined Lee filtering.
[0038] Step 2: Based on the polarization coherence matrix T of the polarization SAR image, obtain t...
Example Embodiment
[0051] Example 2
[0052] The polarization SAR image classification method based on CNN and SVM is the same as that in Example 1. In step 2, the original feature of each pixel of the image is obtained according to the following steps:
[0053] 2a) Since the polarization coherence matrix T has rich phase and amplitude information about the radar target, and both are positive semi-definite Hamiltonian matrices, the diagonal elements of the polarization coherence matrix T with a dimension of 3×3 can be extracted, And the real and imaginary parts of the 3 elements except the diagonal elements in the upper triangle position. Each pixel has a total of 9-dimensional features, expressed as a 3×3 matrix
[0054] T 11 T twenty two T 33 r e a l ( T 12 ) i m a g ( T 12 ) r e a l ( T 13 ) i m a g ( T 13 ) r e a l ( T twenty three ) i m a ...
Example Embodiment
[0057] Example 3
[0058] The polarization SAR image classification method based on CNN and SVM is the same as that of Example 1-2, in which the repeated training of the AE network in step 5 to obtain the initial CNN convolutional layer parameters is performed according to the following steps:
[0059] 5a) Since the convolutional layer of CNN involves local perception areas, it is necessary to randomly select N×N image blocks in each training sample. The image block size selected in the present invention is 5×5;
[0060] 5b) Then use the image blocks selected in 5a) to train the AE network. The AE network has 3 layers: input layer, hidden layer, and output layer. Its working principle is to use the output layer to approximate the input layer to obtain another representation of the input layer characteristics , That is, the hidden layer feature; in the present invention, the number of convolution kernels of the CNN convolution layer is set to 20, which is the same as the number of neu...
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