Polarization SAR image classification method based on Wishart deep network

A deep network and classification method technology, applied in the field of image processing, can solve the problem of high labor intensity, achieve the effect of good classification performance and stable results

Active Publication Date: 2015-11-11
XIDIAN UNIV
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Problems solved by technology

[0007] The extraction methods of the above features are all manually designed according to the problem to be solved and the characteristics of the data, so the labor intensity is particularly large

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  • Polarization SAR image classification method based on Wishart deep network
  • Polarization SAR image classification method based on Wishart deep network
  • Polarization SAR image classification method based on Wishart deep network

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Embodiment Construction

[0034] refer to figure 1 , the concrete realization steps of the present invention are as follows:

[0035] Step 1: Input the polarimetric SAR image to be classified, and use the refined polarimetric LEE filtering method to filter the polarimetric SAR image to be classified to remove speckle noise and obtain a filtered polarimetric SAR image.

[0036] (1a) Set the sliding window size of the refined polarized LEE filter to 7*7 pixels;

[0037] (1b) Slide the sliding window on the pixels of the input polarimetric SAR image from left to right and from top to bottom. When sliding each step, move the sliding window from left to right and top to bottom according to the pixel space position. Divide into 9 sub-windows in turn, there is overlap between the sub-windows, the size of each window is 3*3 pixels, calculate the power mean of each sub-window, that is, the sum of the diagonals of the C matrix, and form the obtained mean 3*3 mean window of pixels;

[0038] (1c) Select the gra...

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Abstract

The invention discloses a polarization SAR image classification method based on a Wishart deep network and mainly aims at solving problems that current feature extraction needs a lot of prior knowledge and a manual labor intensity is high. The method comprises the following steps of (1) inputting a polarization SAR image and carrying out filtering processing; (2) constructing a multilayer Wishart RBM learning feature to the image after filtering; (3) using a learned feature to train softmax classifier; (4) using the multilayer Wishart RBM and the softmax classifier to construct a deep network DBN and training the deep network DBN; (6) using the deep network DBN to classify the polarization SAR image and outputting a result. Compared to a classic classification method, by using the method of the invention, a classification correct rate is high; a classification-result homogeneous area is complete; area consistency is good and classification performance is good too. The method is suitable for carrying out terrain classification and target identification on the polarization SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for classifying polarimetric synthetic aperture radar SAR images, which can be used to realize ground object classification and target recognition of polarimetric SAR images. Background technique [0002] Synthetic Aperture Radar SAR system can obtain remote sensing images with all-weather, all-day time and high resolution. Polarimetric Synthetic Aperture Radar PolSAR is an advanced SAR system that describes the observed land cover and Target. [0003] In the past two decades, studies have shown that PolSAR can provide more useful information than monopolar SAR in target detection, object classification, parameter inversion, and terrain extraction applications. Today, some spaceborne platforms, such as TerraSAR-X satellites, RADARSAT-2 satellites, and ALOS-PALSAR satellites, continue to provide huge amounts of polarimetric SAR data. Manual interpretation of th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/21G06F18/24155
Inventor 王爽焦李成郭岩河高琛琼刘红英史丹荣张东辉滑文强
Owner XIDIAN UNIV
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