Polarized SAR image classification method based on DCCGAN

A classification method and model technology, applied in the field of image processing, can solve the problems of slow model training, lack of accuracy, and inability to detect image edges, achieve rapid convergence to the global optimal solution, improve classification accuracy, and improve The effect of classification efficiency

Active Publication Date: 2018-04-20
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that the method extracts scattering features through Pauli decomposition, does not take into account the rich information of the original polarimetric SAR complex data, and ignores some phase information of the image, resulting in insufficient utilization of image information and poor image quality. The edge of the image is detected, and the resu

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  • Polarized SAR image classification method based on DCCGAN
  • Polarized SAR image classification method based on DCCGAN
  • Polarized SAR image classification method based on DCCGAN

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] refer to figure 1 , the steps that the present invention realizes are as follows:

[0038] Step 1, input a polarimetric SAR image in which each pixel is a 2×2 polarimetric scattering matrix to be classified.

[0039] Step 2, preprocessing data.

[0040] The real-virtual separation method is used to extract features from each pixel in the polarimetric SAR image to be classified, and the 8-dimensional real number feature matrix of the polarimetric SAR image is obtained.

[0041] Extract the real value of the echo data from each complex element of the following matrix:

[0042]

[0043]Among them, S represents the polarization scattering matrix of each pixel of the polarization SAR image to be classified, [] represents the matrix symbol, and A represents the vertically transmitted echo received in the vertical direction in the input polarization scattering ma...

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Abstract

The invention discloses a polarized SAR image classification method based on DCCGAN. The method comprises steps of (1) inputting an image; (2) preprocessing data; (3) normalizing a feature matrix andextracting blocks; (4) constructing a data set; (5) constructing a DCCGAN model; (6) training the DCCGAN model; (7) constructing and initializing a discriminant classification network model; (8) training the discriminant classification network model; and (9) predicting classification. The method is not required to decompose the polarized target of the polarized SAR image, can extract the featuresdirectly from a polarization scattering matrix, makes full use of the rich information of the polarized SAR image, and improves the classification precision of the polarized SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarimetric synthetic aperture radar based on a deep complex convolutional generative confrontation network DCCGAN (Deep Complex Convolutional Generative Adversarial Network) model in the technical field of polarimetric synthetic aperture radar image feature classification SAR (Synthetic ApertureRadar) image classification method. The invention can be used to classify ground objects in polarimetric SAR images, can effectively improve the classification accuracy of polarimetric SAR images, and can be used for object identification, tracking and positioning. Background technique [0002] Polarization SAR is a high-resolution active microwave remote sensing imaging radar, which has the advantages of all-weather, all-time, high resolution, side-view imaging, etc., and can obtain richer information on targets. The purpose of polarimetric SAR image classification is ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/13G06N3/045G06F18/214
Inventor 焦李成屈嵘张婷马晶晶杨淑媛侯彪马文萍刘芳张丹唐旭陈璞华古晶
Owner XIDIAN UNIV
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