Polarized SAR image classification method based on stacked code and softmax

A technology of stack coding and classification methods, which is applied in the field of polarization synthetic aperture radar image classification, and can solve problems such as high manual labor intensity

Active Publication Date: 2014-11-19
XIDIAN UNIV
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Problems solved by technology

[0007] The method of extracting these features is designed manually according to the problem to be solved and the c

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  • Polarized SAR image classification method based on stacked code and softmax
  • Polarized SAR image classification method based on stacked code and softmax
  • Polarized SAR image classification method based on stacked code and softmax

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Embodiment Construction

[0050] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0051] Step 1, read in a polarimetric SAR image to be classified, filter the polarimetric SAR image to be classified by using the refined polarization LEE filtering method, remove speckle noise, and obtain the filtered polarimetric SAR image;

[0052] (1a) Input an optional polarimetric SAR image to be classified.

[0053] (1b) Use the refined polarization LEE filtering method to filter the polarization SAR image to be classified:

[0054] The first step is to set the sliding window size of refined polarization LEE filtering to 7×7 pixels;

[0055] In the second step, the sliding window is slid from left to right and from top to bottom on the pixels of the input polarimetric SAR image, and at each sliding step, the sliding window is moved from left to right and from top to bottom according to the pixel space position. The sub-windows are divided into 9 sub-windows sequent...

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Abstract

The invention belongs to the technical field of image processing, particularly discloses a polarized SAR image classification method based on a stacked code and softmax, and mainly aims to solve the problem that conventional characteristic extraction requires much priori knowledge, and is high in manual labor intensity. The method comprises the following steps: (1) inputting an image and filtering; (2) extracting independent elements; (3) selecting a trained sample and a tested sample and conducting whitening process; (4) constructing a stacked code network to learn superior characteristics better representing the SAR image; (5) training a classifier, and forecasting classification result; (6) calculating accuracy; (7) outputting the result. Compared with the classic classification methods, the method is provided with higher accuracy of ground features classification, is more integrative in uniform district, better in district consistency and classification performance, and can be suitable for ground feature classification and target identification of polarized SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a polarization synthetic aperture radar (Synthetic Aperture Radar) which performs feature learning based on stack coding and classifies with a softmax classifier in the technical field of polarization synthetic aperture radar image feature classification. , SAR) image classification method. It can be used for ground object classification and target recognition of polarimetric SAR images, and can effectively improve the accuracy of polarimetric SAR image classification. Background technique [0002] The synthetic aperture radar (SAR) system can obtain all-weather, all-time, and high-resolution remote sensing images. The polarimetric synthetic aperture radar (polarimetric SAR) is an advanced SAR system that describes observations by transmitting and receiving polarized radar waves. land cover and targets. [0003] In the past two decades, studies have shown t...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 王爽马文萍谢慧明霍丽娜马晶晶雷晓珍
Owner XIDIAN UNIV
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