Polarized SAR Classification Method Based on Scattering Fully Convolutional Model

A convolution model and classification method technology, applied in the field of image processing, can solve problems such as low classification accuracy, incomplete scattering feature information, poor classification effect of image edge pixels, etc., to improve classification accuracy, improve training speed, The effect of speeding up training

Active Publication Date: 2021-09-03
XIDIAN UNIV
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Problems solved by technology

However, the disadvantage of this method is that the scattering feature information extracted by this method is incomplete, so the classification effect on the edge pixels of the image is poor.
Although this method uses multi-target decomposition to obtain comprehensive polarization characteristics, the disadvantage of this method is that the method does not learn the texture characteristics of the image, resulting in incomplete feature information and low classification accuracy. The training speed of SVM is much slower than that of convolutional neural network

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  • Polarized SAR Classification Method Based on Scattering Fully Convolutional Model
  • Polarized SAR Classification Method Based on Scattering Fully Convolutional Model
  • Polarized SAR Classification Method Based on Scattering Fully Convolutional Model

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Embodiment Construction

[0029] Attached below figure 1 The steps of the present invention are further described in detail.

[0030] Step 1. Perform Lee filtering on the polarimetric SAR image to be classified.

[0031] The scattering matrix of the polarimetric SAR image to be classified is subjected to refined polarization Lee filtering to filter coherent noise, and the filtered scattering matrix is ​​obtained. The size of the polarimetric SAR image to be classified is 1800×1380 pixels, and the filtered scattering matrix is ​​obtained. Each element in the matrix is ​​a 3×3 matrix, which is equivalent to a 9-dimensional feature for each pixel.

[0032] The window size of the Lee filter in the refined polarized Lee filter is 7×7 pixels.

[0033] Step 2. Perform Pauli decomposition of the scattering matrix.

[0034] The filtered scattering matrix is ​​decomposed by Pauli to obtain odd scattering energy, even scattering energy and volume scattering energy, and use the decomposition obtained odd scatte...

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Abstract

The invention discloses a polarization SAR classification method based on a scattering full convolution model, the steps of which are: (1) performing Lee filtering on the polarization SAR image to be classified; (2) performing Pauli decomposition on the scattering matrix; (3) Normalization of the feature matrix; (4) Constructing a data set; (5) Constructing a scattering full convolution network model; (6) Training a scattering full convolution model; (7) Obtaining test results. The invention effectively combines the polarization characteristics, scattering characteristics and texture characteristics of the polarimetric SAR image, retains the integrity of the feature information, improves the classification accuracy of the image, and accelerates the training speed at the same time.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarization synthetic aperture radar (Synthetic Aperture Radar, SAR) image classification method based on a scattering (Scatter) full convolution model in the technical field of polarization synthetic aperture radar image ground object classification . The invention can be used to classify the ground objects of the polarimetric SAR images, can effectively improve the classification accuracy of the polarimetric SAR images, and can be used for the classification of the ground objects of the polarimetric SAR images and target recognition. Background technique [0002] Polarization synthetic aperture radar has many outstanding advantages, such as not being affected by time, and can image 24 hours a day. Polarization SAR images have unique advantages and broad application prospects, and have been successfully used in land use classification, change detection, surfac...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/241
Inventor 焦李成屈嵘高丽丽马文萍杨淑媛侯彪刘芳唐旭马晶晶张丹陈璞华古晶
Owner XIDIAN UNIV
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