Sparse depth network based polarization SAR (Synthetic Aperture Radar) image classification

A deep network and sparse technology, applied in the direction of instrument, character and pattern recognition, scene recognition, etc., can solve the problems of increased time complexity, difficulty in network training, loss of original images, etc., to improve accuracy and classification efficiency, and improve image quality , the effect of convenient calculation

Inactive Publication Date: 2015-01-28
XIDIAN UNIV
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Problems solved by technology

However, each feature extraction method has a corresponding best use occasion, and it is inevitable to lose part of the information of the original image, so it is difficult to find a particularly suitable feature
At present, the deep network has attracted extensive attention from researchers due to its powerful learning abil...

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  • Sparse depth network based polarization SAR (Synthetic Aperture Radar) image classification
  • Sparse depth network based polarization SAR (Synthetic Aperture Radar) image classification
  • Sparse depth network based polarization SAR (Synthetic Aperture Radar) image classification

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Embodiment Construction

[0026] Refer to attached figure 1 , the implementation steps of the present invention are as follows:

[0027] Step 1. Input image

[0028] Input any polarimetric SAR image to be classified.

[0029] Step 2. Filtering

[0030] The refined polarimetric LEE filtering method is used to filter the polarimetric SAR image to be classified to remove the speckle noise and obtain the filtered polarimetric SAR image.

[0031] Set the sliding window of refined polarization LEE filtering, the size of the sliding window is 5*5 pixels.

[0032] The sliding window is roamed from left to right and from top to bottom on the pixels of the input polarimetric SAR image. At each roaming step, the sliding window is divided into 9 sub-sections from left to right and from top to bottom according to the spatial position of the pixel. Window, the size of each sub-window is 3*3 pixels, and there is overlap between sub-windows.

[0033] The pixel values ​​at the corresponding positions of the 9 sub-...

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Abstract

The invention discloses a sparse depth network based polarization SAR (Synthetic Aperture Radar) image classification method. The implementation of the sparse depth network based polarization SAR image classification method comprises step 1, inputting an image; step 2, performing filtering; step 3, extracting features; step 4, constructing and training a sparse depth network; step 5, performing prediction and classification; step 6, outputting a result. Compared with the existing method, an energy function can be minimized, a distortion function can be controlled within a certain range to enable a hidden layer to be sparse, and accordingly the calculation is greatly reduced, the generalization capability is high, and the classification accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to the technical field of polarimetric SAR image ground object classification. Background technique [0002] Polarization SAR is a high-resolution active microwave remote sensing imaging radar. It has the advantages of all-weather, all-time, high resolution, and side-view imaging. It can be used in many fields such as military, agriculture, navigation, and geographical surveillance. . Compared with SAR, polarimetric SAR performs full polarimetric measurement, which can obtain richer information about the target. In recent years, the classification using polarimetric SAR data has received great attention in the field of international remote sensing, and has become the main research direction of image classification. [0003] The general process of polarimetric SAR image classification includes the steps of image data acquisition, image feature extraction, training cl...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/66
CPCG06V20/13G06F18/217
Inventor 焦李成杨淑媛高蓉马文萍王爽侯彪刘红英熊涛马晶晶张向荣
Owner XIDIAN UNIV
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