SAR Image Segmentation Method Based on Ridgelet Filter and Deconvolution Structure Model

Abstract

The invention discloses an SAR image segmentation method based on a ridge wave filter and a deconvolution structural model, mainly used for solving the problem that SAR image segmentation in the prior art is inaccurate. The SAR image segmentation method comprises the following implementation steps of: (1), sketching an SAR image so as to obtain a sketch image; (2), according to an area chart of the SAR image, dividing a pixel subspace of the SAR image; (3), constructing a ridge wave filter set; (4), constructing a deconvolution structural model; (5), segmenting a hybrid aggregation structured surface feature pixel subspace by adopting the SAR image segmentation method based on the ridge wave filter and the deconvolution structural model; (6), performing independent target segmentation based on the sketch line aggregation feature; (7), performing line target segmentation based on a visual semantic rule; (8), performing segmentation of a pixel subspace in a homogeneous area by adopting a polynomial-based logistic regression prior model; and (9), combining segmentation results so as to obtain an SAR image segmentation result. By means of the SAR image segmentation method disclosed by the invention, the good segmentation effect of the SAR image is obtained; and the SAR image segmentation method 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 synthetic aperture radar (SAR) image segmentation method based on a ridgelet filter and a deconvolution structure model in the technical field of target recognition. The invention can accurately segment the regions with different characteristics of the synthetic aperture radar SAR image, and can be used for target detection and recognition of the subsequent synthetic aperture radar 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 techno...

AI Technical Summary

Problems solved by technology

Although this method achieves the purpose of autonomous learning of image features, the disadvantage of this method is that for the convenience of processing, this method normalizes the input image, which destroys the original structure of the image. information

Although this method achieves unsupervised learning of image features, the disadvantage of this method is that when initializing the filter, the method of randomly initializing the ridgelet filter is used, and the structural information of the image is ignored, so that It will greatly reduce the accuracy of image segmentation

The shortcomings of this method are that the boundary positioning of the aggregated area is not precise enough, the determination of the number of homogeneous areas is not reasonable enough, the regional consistency of the segmentation results is poor, and the independent target is not processed in the segmentation of the structural area.

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