Multicore fusion and spatial Wishart LapSVM-based semi-supervised polarimetric SAR image classification method

A technology of multi-core fusion and classification method, applied in the field of image processing, to achieve the effect of improving classification accuracy, improving classification effect, and solving heterogeneous characteristics

Active Publication Date: 2017-12-19
SUZHOU WENJIE SENSING TECH
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Problems solved by technology

[0005] Since the current supervised and unsupervised classification methods for polarimetric SAR images have certain limitations...

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  • Multicore fusion and spatial Wishart LapSVM-based semi-supervised polarimetric SAR image classification method
  • Multicore fusion and spatial Wishart LapSVM-based semi-supervised polarimetric SAR image classification method
  • Multicore fusion and spatial Wishart LapSVM-based semi-supervised polarimetric SAR image classification method

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Embodiment Construction

[0028] The present invention is a semi-supervised polarization SAR image classification method based on multi-core fusion and space Wishart LapSVM, see figure 1 , the specific implementation steps of the present invention are as follows:

[0029] Step 1. Input the polarimetric SAR image to be classified, and obtain its polarization coherence matrix T.

[0030] see figure 2 , the polarized SAR image is a Dutch farmland map, and its object categories to be classified include bare land, potato, sugar beet, barley, pea and wheat. See Figure 3 for the picture.

[0031] The present invention realizes the semi-supervised classification of ground objects on polarimetric SAR images, and the actual classification effect of the present invention is verified by the classification experiment of 6 types of ground objects in the figure.

[0032] Step 2. Obtain the polarization feature vector based on the polarization coherence matrix T in the polarimetric SAR image, and perform feature n...

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Abstract

The invention discloses a multicore fusion and spatial Wishart LapSVM-based semi-supervised polarimetric SAR image classification method, and mainly aims at solving the problem that the classification precision is low as labelled samples of synthetic aperture radar full-polarimetric SAR images are relatively few in existing classification method. The method comprises the following steps of: obtaining a polarization related matrix T; extracting polarization feature vectors of the matrix T and carrying out normalization processing; establishing a training sample set; constructing a Spatial-Wishart manifold regular term; and calculating classification correctness and outputting a polarimetric SAR image classification result. According to the method, the problem that the traditional non-supervision polarimetric SAR image classification is low in correctness is solved, the malpractices of difficulty artificial labelling and high cost caused by requirement of massive label data are avoided; and by jointly utilizing less labelled data and massive un-labelled cheap data, a better classification effect is obtained, so that the method can be used for target classification, detection and recognition of polarimetric SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a polarimetric SAR image classification method, and can be used in technical fields such as ground object classification and target recognition of the polarimetric SAR image. Background technique [0002] Polarimetric SAR (Polarimetric SAR) is a synthetic aperture radar capable of full polarization measurement of the target. It performs full polarization measurement and imaging of the target by measuring and recording the phase difference information of the combined echoes of different polarization states. Polarimetric SAR data contains richer target scattering information, which can more comprehensively express and describe the target, and improve the ability to identify ground objects. At the same time, it has the advantages of all-weather, all-time, and high resolution. It has outstanding advantages in identification, classification and parameter inversion, so it is widely ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/13G06F18/2411G06F18/2415
Inventor 王敏王勇
Owner SUZHOU WENJIE SENSING TECH
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