Mean Field Variational Bayesian SAR Image Segmentation Method Based on Sketch Structure

Abstract

The present invention discloses a sketch structure-based mean field variational Bayes synthetic aperture radar (SAR) image segmentation method. The method mainly solves the problem in the prior art that the SAR image is not segmented accurately. The method comprises a first step of sketching the SAR image, so as to obtain a sketch of the SAR image; a second step of dividing pixel sub-spaces of the SAR image according to an area chart of the SAR image; a third step of segmenting based on the pixel sub-spaces of a hybrid gathering structure of a mean field variational Bayes deduction network model; a fourth step of segmenting based on an independent target of a sketch line gathering feature; a fifth step of segmenting based on a line target of a visual semantic rule; a sixth step of segmenting based on a homogeneous area pixel sub-space of a polynomial logic regression prior model; and a seventh step of combining segmentation results, so as to obtain a segmentation result of the SAR image. Through adoption of the method, a good segmentation effect of the SAR image is obtained and can be used for semantic segmentation of the SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a mean field variation Bayesian synthetic aperture radar SAR (Synthetic Aperture Radar) image segmentation method based on a sketch structure in the technical field of image segmentation. The invention can be applied to SAR image segmentation, and can accurately segment different regions of the SAR image. Background technique [0002] Synthetic aperture radar (SAR) is an important progress in the field of remote sensing technology, which is used to obtain high-resolution images of the earth's surface. Compared with other types of imaging technologies, SAR has a very important advantage. It is not affected by atmospheric conditions such as clouds, rainfall or heavy fog, and light intensity, and can obtain high-resolution remote sensing data all day and all weather. SAR technology has important guiding significance for military, agriculture, geography and many other...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that when obtaining the feature vector of the SAR image, the pixel-level features of the SAR image are used, and the unique structural features of the SAR image due to the correlation between pixels are not learned, resulting in segmentation results Not accurate enough

The disadvantage of this method is that the weights of the deep self-encoding network used to automatically extract image features are initialized randomly, and the unique distribution of SAR images is not used, and the sketch structure constraints of SAR images are not added when training the network. Therefore, the essential features of the image cannot be effectively extracted, which reduces the accuracy of SAR image segmentation

The disadvantage of this method is that when comparing and inferring the structural features of the disconnected regions in the aggregation region, this method uses the reasoning method of the self-organizing feature map SOM network, which needs to be manually determined. The number of clusters, and the clustering time is long, resulting in a decrease in clustering accuracy and affecting the accuracy of SAR image segmentation

The disadvantages of this method are that the boundary positioning of the aggregated area is not precise enough, the number of categories for the homogeneous area is not reasonable enough, the regional consistency of the segmentation result is poor, and the independent target is not processed in the segmentation result of the structural area.

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