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Polarimetric SAR semi-supervised classification method based on superpixel correlation matrix

A technology of correlation matrix and classification method, applied in the field of image processing

Active Publication Date: 2014-07-16
XIDIAN UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to address the deficiencies of the above-mentioned technologies, and propose a semi-supervised classification method for polarimetric SAR images based on superpixel correlation matrix, to reduce the influence of coherent speckle noise on image processing, and to reduce the impact of coherent speckle noise on image processing. improve the classification accuracy

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  • Polarimetric SAR semi-supervised classification method based on superpixel correlation matrix
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  • Polarimetric SAR semi-supervised classification method based on superpixel correlation matrix

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[0042] Attached below figure 1 The steps of the present invention are further described in detail.

[0043] Step 1: Preprocessing the polarimetric SAR image with decoherent speckle noise and synthesizing a pseudo-color image:

[0044] 1.1) Read in a piece of polarimetric SAR data, use the refined Lee filter to preprocess it to reduce coherent speckle noise, and obtain the corresponding covariance matrix, and the window size of the filter is set to 7×7.

[0045] 1.2) Perform Pauli energy eigendecomposition on the covariance matrix, and synthesize the pseudo-color image corresponding to the polarimetric SAR data.

[0046] Step 2: Perform over-segmentation on the pseudo-color image, and calculate the area center of the test superpixel and the training superpixel respectively.

[0047] 2.1) Use the Normalized cut method to over-segment the pseudo-color image to obtain several superpixels, S 1 ,S 2 ,…S i ,…S k , take these superpixels as test superpixels, where S i Represent...

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Abstract

The invention discloses a polarimetric SAR semi-supervised classification method based on a superpixel correlation matrix. The polarimetric SAR semi-supervised classification method mainly solves the problem that an existing technology needs large quantity of samples. The achieving steps are as follows: (1) reading a polarimetric SAR image, conducting related preprocessing on speckle noise on the SAR image, and synchronizing a pseudo-color image; (2) calculating and testing a superpixel, training the area center of the superpixel, and constructing a data array and a physical feature correlation matrix; (3) calculating a sparsity structure feature correlation matrix through a data matrix; (4) conducting weight fusion on the physical feature correlation matrix and the sparsity structure feature correlation matrix; (5) classifying the related fused arrays through a semi-supervised method, and outputting a final classification result. The polarimetric SAR semi-supervised classification method reduces the influence of the speckle noise on the classification result, effectively reduces the requirements on the quantity of training samples, improves accuracy rate of classification, and can be used for classifying and identifying surface features.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a semi-supervised classification method for polarimetric SAR images, which can be used for target recognition. Background technique [0002] Synthetic Aperture Radar (SAR) is an effective means of earth observation from space. It has the characteristics of all-weather and all-weather work. It has been widely used in geological exploration, urban planning, military detection and so on. PolSAR is a coherent multi-channel microwave imaging system produced by the continuous development of SAR technology. It obtains the polarization information of the target by measuring the scattering characteristics of each resolution unit on the ground under different polarization combinations. Compared with polarimetric SAR, PolSAR records the backscatter information of the target more completely, thus greatly enhancing the radar's ability to obtain target information. [0003] At presen...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/66
Inventor 焦李成刘芳高晓莹杨淑媛马文萍马晶晶王爽侯彪符丹钰
Owner XIDIAN UNIV
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