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Deep wavelet neural network-based polarimetric SAR (synthetic aperture radar) image classification method

A wavelet neural network and classification method technology, applied in the field of image processing, can solve the problems of inability to extract high-dimensional features and low classification accuracy, and achieve the effects of improving classification accuracy, strong approximation and fault tolerance, and good robustness.

Active Publication Date: 2015-03-11
XIDIAN UNIV
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  • Summary
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Problems solved by technology

However, the disadvantage of this method is that since this method directly uses the coherence matrix of polarimetric SAR images to train the S4VMs classifier, it cannot extract higher-dimensional features from the coherence matrix, resulting in low classification accuracy.

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  • Deep wavelet neural network-based polarimetric SAR (synthetic aperture radar) image classification method
  • Deep wavelet neural network-based polarimetric SAR (synthetic aperture radar) image classification method
  • Deep wavelet neural network-based polarimetric SAR (synthetic aperture radar) image classification method

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Embodiment 1

[0035] The present invention is a kind of classification method of polarimetric synthetic aperture radar SAR image based on depth wavelet neural network, referring to the attached figure 1 , describe in detail the specific implementation steps of the present invention:

[0036]Step 1: Input an image, actually input a coherence matrix of a polarimetric SAR image to be classified, see for details figure 2 , figure 2 Shown is the L-band multi-view polarimetric SAR image in the Flevoland, Netherlands region obtained by the AIRSAR platform in 1989. The coherence matrix of the image is a matrix with a size of 3×3×N, and N is the polarimetric synthetic aperture radar SAR image The total number of pixels.

[0037] Step 2: Preprocessing, use a Lee filter with a window size of 7×7 to filter the above coherence matrix to obtain the filtered coherence matrix, in specific simulation experiments, you can use 3×3, 5×5, 7 The coherence matrix of the polarimetric SAR image is filtered wit...

Embodiment 2

[0055] The polarization synthetic aperture radar SAR image classification method based on deep wavelet neural network is the same as embodiment 1, wherein the calculation hidden layer output formula described in step 4b and step 4e is as follows:

[0056] ψ ( j ) = exp ( - ( Σ k = 1 m W jk ′ x k - b j a j ...

Embodiment 3

[0063] The polarization synthetic aperture mine SAR image classification method based on the depth wavelet neural network is the same as embodiment 1-2, and the mean square error formula described in step 4c and step 4f is as follows:

[0064] E = Σ s = 1 S Σ i = 1 n ( h ( i ) - x i ) 2

[0065] Among them, E represents the mean square error of the sample, where E is a general representation of the mean square error, and the error of the first layer network is represented by E 1 Indicates that the error of the first layer network is expressed by E 2 Indicat...

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Abstract

The invention discloses a deep wavelet neural network-based polarimetric SAR (Synthetic Aperture Radar) image classification method, and aims to mainly solve the problem of classification accuracy reduction caused by fewer characteristics or unreasonable characteristic extraction in the prior art. The method is implemented by the following steps: inputting an image; performing preprocessing; selecting samples; training a deep wavelet neural network by utilizing a training sample; extracting characteristics; performing classification; calculating classification accuracy. According to the method, the deep wavelet neural network is trained layer by layer, so that the problem of gradient diffusion in case of more network layers is solved; moreover, high-dimensional characteristics capable of reflecting essential properties of data, describing detail characteristics of the data and highlighting differences between different ground object types can be extracted; the deep high-dimensional characteristics of the data are extracted by virtue of the deep wavelet neural network, so that the problem of fewer characteristics or incomplete and unreasonable characteristic learning in a classification technology is successfully solved, and the classification accuracy of a polarimetric SAR image is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to the processing of polarimetric SAR images, in particular to a method for classifying polarimetric SAR images based on a deep wavelet neural network. It can be used to classify and identify ground targets in polarimetric SAR images. Background technique [0002] Synthetic aperture radar is a high-resolution imaging radar. Because microwaves have penetrating properties and are not affected by light intensity, synthetic aperture radars have all-day and all-weather working capabilities. Compared with other sensor images, it can show more details and can better distinguish the characteristics of nearby objects. With the development of technology, synthetic aperture mines are gradually developing in the direction of high resolution, multi-polarization, and multi-channel. Compared with traditional single-polarization SAR, multi-polarization SAR can provide richer target info...

Claims

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Application Information

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IPC IPC(8): G06K9/66G06N3/08
CPCG06N3/082G06F18/2411
Inventor 焦李成李玲玲姜红茹屈嵘杨淑媛侯彪王爽刘红英熊涛马文萍马晶晶
Owner XIDIAN UNIV
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