G0 distribution-based stochastic gradient variational Bayesian SAR image segmentation method

Abstract

The invention discloses a G0 distribution-based stochastic gradient variational Bayesian SAR image segmentation method. The method comprises the steps of extracting a sketch drawing of an SAR image according to an initial sketch model; according to a regional chart, dividing the SAR image into a mixed pixel sub-space, a uniform pixel sub-space and a structure pixel sub-space; for each extremely non-uniform region in the mixed pixel sub-space, estimating a G0 distribution parameter of each extremely non-uniform region, and learning structural features of each extremely non-uniform region by utilizing a G0 distribution-based stochastic gradient variational Bayesian model, thereby realizing unsupervised segmentation of the mixed pixel sub-space; and performing corresponding segmentation on the uniform pixel sub-space and the structure pixel sub-space, and fusing segmentation results of the three sub-spaces to obtain an SAR image segmentation result finally. According to the method, hidden variable prior distribution and similar posterior distribution in the model are both assumed to meet G0 distribution of extremely non-uniform regions, and a corresponding analysis formula is derived for performing learning, so that the accuracy of clustering the extremely non-uniform regions in the mixed pixel sub-space is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a G-based 0 The distributed stochastic gradient variational Bayesian SAR image segmentation method can be applied to accurately segment different regions of synthetic aperture radar SAR and target detection and recognition in SAR images. 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 fields. [0003] Image segmentation refers to the process of dividing an ...

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 automatically learned. Insufficient use of structural features that truly represent the characteristics of SAR image features, resulting in inaccurate segmentation results

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