Identification method of image target of synthetic aperture radar based on noise independent component analysis

A synthetic aperture radar, independent component analysis technology, applied in character and pattern recognition, reflection/re-radiation of radio waves, measurement devices, etc., can solve the problem that the NFastICA algorithm cannot be directly applied, and improve the recognition accuracy and efficiency. , to overcome the influence, to improve the effect of denoising

Inactive Publication Date: 2010-12-08
BEIHANG UNIV
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However, the two important prerequisites of the NFastICA algorithm are that the data noise is Gaussian distribution (that is, normal distribution) and the noise covariance matrix is ​​known, but the noise in the SAR image is mostly multiplicative noise, and except for the

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  • Identification method of image target of synthetic aperture radar based on noise independent component analysis
  • Identification method of image target of synthetic aperture radar based on noise independent component analysis
  • Identification method of image target of synthetic aperture radar based on noise independent component analysis

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[0033] Specific implementation plan

[0034] The synthetic aperture radar image target recognition method based on noise independent component analysis proposed by the present invention includes the following steps:

[0035] (1) For the original training sample image X of the input synthetic aperture radar train Pretreatment, the specific process is as follows:

[0036] (1-1) For training sample image X train Perform logarithmic transformation to get X ln =20ln(1+X Orig ), where X Orig Represents a single image of the input original training sample, X ln Represents an image whose noise distribution conforms to the Gaussian distribution after logarithmic transformation;

[0037] (1-2) To the above X ln Perform de-averaging processing to obtain a zero-mean image X t , X t =X ln -E(X ln ), where the expectation function E represents averaging the image;

[0038] (1-3) The above zero mean image X t Divide into target area X o And noise area n o , And satisfy {X t }={n o }∪{X o }, the two-...

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Abstract

The invention relates to an identification method of image targets of a synthetic aperture radar based on noise independent component analysis, belonging to the technical filed of synthetic aperture radar image target identification. The method comprises the following steps: firstly, preprocessing input images of an original training sample of the synthetic aperture radar to ensure that the images conform to whitening and zero mean normalization and the probability density of image noise complies with Gaussian distribution; secondly, performing logarithm noise independent component analysis on the preprocessed training sample and a real-time measurement sample to be identified, extracting the independent component characteristics of the images to be identified; and finally, identifying the sample to be identified by an independent component analysis method. The independent component characteristics extracted by the method of the invention are more suitable for classification, thus eliminating the image denoising process necessarily to be carried out in the preprocessing stage in an ideal ICA algorithm, improving the robustness of the algorithm on abnormal data, improving the instantaneity and reliability of SAR image target identification, and being suitable for SAR image characteristic extraction and automatic target identification.

Description

technical field [0001] The invention relates to a synthetic aperture radar image target recognition method based on noise independent component analysis, and belongs to the technical field of synthetic aperture radar image target recognition. Background technique [0002] Synthetic Aperture Radar (hereinafter referred to as SAR) forms SAR images according to the backward electromagnetic scattering of targets, which can overcome the shortcomings of optical imaging limited by distance, climate and other conditions. , image matching guidance and other fields play a vital role. However, in the process of SAR imaging, there are usually many scatterers distributed in the same resolution unit, and the phases of the echo signals of these scatterers are randomly distributed, and the mutual interference leads to the inherent multiplicative speckle noise of the SAR image. Compared with ordinary electron optical images, general SAR images have the characteristics of low resolution, sen...

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IPC IPC(8): G06K9/00G06K9/54G01S7/41G01S13/90
Inventor 赵巍丁卓姝马浩然
Owner BEIHANG UNIV
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