Semi-supervised polarization SAR terrain classification method based on full convolution GAN

A ground object classification and semi-supervised technology, applied in the field of image processing, can solve the problem of inaccurate classification results, improve classification accuracy, overcome repeated storage and calculation of convolution, and enhance robustness.

Inactive Publication Date: 2018-09-21
XIDIAN UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to provide a semi-supervised polarization SAR object classification method based on full convolution GAN, which solves the defect that the classification results of the current polarization SAR classification method are not accurate enough

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  • Semi-supervised polarization SAR terrain classification method based on full convolution GAN
  • Semi-supervised polarization SAR terrain classification method based on full convolution GAN
  • Semi-supervised polarization SAR terrain classification method based on full convolution GAN

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Embodiment Construction

[0040] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0041] Such as figure 1 As shown, the semi-supervised polarization SAR object classification method based on full convolution GAN provided by the present invention comprises the following steps:

[0042] Step 1. Refined Lee filtering:

[0043] Exquisite Lee filtering is performed on an original polarimetric SAR image to obtain the filtered polarimetric SAR image to be classified;

[0044] Step 2. Enter data:

[0045] The real polarimetric SAR image to be classified and its corresponding label image are taken as true samples; wherein, the label image and the polarimetric SAR image have the same size;

[0046] Step 3. Build the generative network G:

[0047] The generation network G is a convolutional neural network comprising four deconvolution layers, the first layer of the network is a convolution kernel size of 2 × 2, a convolution step size of 1, and a...

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Abstract

A semi-supervised polarization SAR terrain classification method based on full convolution GAN is implemented by the following steps: performing refined Lee filtering; inputting polarization SAR data;constructing a generation network G; constructing a discrimination network D; training and testing the networks and calculating classification accuracy rate. Compared with the semi-supervised Co-training classification method in the prior art, the invention improves the classification accuracy of polarization SAR data of the polarized synthetic aperture radar, solves the problem of insufficient utilization of polarization information, enhances the robustness of the model to the input data, and has the effect of end-to-end classification. The invention can be applied to the terrain classification of polarized synthetic aperture radar polarized SAR data.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a semi-supervised polarimetric SAR object classification method based on full convolution GAN. Background technique [0002] Polarization synthetic aperture radar (polarization SAR) uses different polarization methods to alternately transmit and receive radar signals, and can obtain extremely rich target scattering information. It has become an important tool for ground detection, and with its all-day and all-weather work It has been at the forefront of remote sensing information acquisition technology, and has extensive research and application value in agriculture, forestry, military, geology, hydrology and oceans, such as the identification of ground object types, crop growth monitoring, and yield evaluation. , ground object classification, sea ice monitoring, land subsidence monitoring, target detection and marine pollution detection, etc. The successful appli...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/088G06N3/045G06F18/214G06F18/24
Inventor 王爽焦李成胡月刘梦晨郭岩河张丹赵阳孙莉
Owner XIDIAN UNIV
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