Unsupervised sar image segmentation method based on high-order multi-scale crf

An image segmentation, multi-scale technology, applied in the field of image processing, can solve the problem of low accuracy, reduce the demand for training data, and overcome the effect of low accuracy

Active Publication Date: 2020-10-09
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that the convolutional neural network needs enough training data to learn network parameters, and it is not suitable for the situation where there is only one synthetic aperture radar SAR image
The disadvantage of this method is that this method ignores the scattering statistical characteristics of the synthetic aperture radar SAR image, which makes the accuracy of the segmentation result not high

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  • Unsupervised sar image segmentation method based on high-order multi-scale crf
  • Unsupervised sar image segmentation method based on high-order multi-scale crf
  • Unsupervised sar image segmentation method based on high-order multi-scale crf

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Embodiment Construction

[0067] The present invention will be further described below in conjunction with the accompanying drawings.

[0068] refer to figure 1 , to further describe the specific implementation steps of the present invention.

[0069] Step 1, input synthetic aperture radar SAR image.

[0070] In step 2, the input SAR SAR image is subjected to wavelet transform to obtain a multi-scale SAR SAR image.

[0071] Step 3, calculate the histogram features of the high-order multi-scale conditional random field CRF.

[0072] A window with a radius of 7 pixels is used to slide at intervals of one pixel in the multi-scale synthetic aperture radar SAR image, and the sliding window operation is performed on the multi-scale synthetic aperture radar SAR image, and all pixels in each sliding window are calculated separately Histogram features of values.

[0073] The specific steps for calculating the histogram features of all pixel values ​​in each sliding window are as follows:

[0074] Step 1, a...

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Abstract

The invention discloses a high-order multi-scale condition random field CRF unsupervised synthetic aperture radar SAR image segmentation method. The method comprises the following steps: performing wavelet transform on the input SAR image; computing histogram features; computing semivariance feature; forming eigenvector; calculating the conditional probability of the local class; performing initial segmenting of synthetic aperture radar sar image; calculating an edge probability of each pixel point; calculating a joint posterior edge probability of each pixel point; calculating a posterior edge probability of each pixel point; estimating parameters; segmenting SAR image segmentation. The invention solves the model parameters through the iteration of the relevant parameters and the iteration of the characteristic parameters, fully utilizes the characteristics of the image itself, and greatly reduces the demand for the quantity of the training data.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an unsupervised synthetic aperture radar SAR (Synthetic Aperture Radar) image segmentation method based on high-order multi-scale CRF (Conditional Random Fields) in the technical field of radar image processing. The invention can be used for segmenting and processing synthetic aperture radar SAR images. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution imaging radar. Its use in civilian and military applications requires the support of synthetic aperture radar SAR image interpretation technology. Synthetic aperture radar SAR image segmentation is an important part of synthetic aperture radar SAR image interpretation technology, which can provide the overall structure of synthetic aperture radar SAR image Therefore, it promotes the application of synthetic aperture radar SAR system in many fields, such as geological exploration and e...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/12G06T5/40
CPCG06T5/40G06T7/12G06T2207/10044
Inventor 张鹏江银银李明宋婉莹谭啸峰
Owner XIDIAN UNIV
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