SAR Image Segmentation Method Based on Curvelet Filter and Convolution Structure Learning

Abstract

The invention discloses a curvelet filter and convolutional structure learning-based SAR image segmentation method, and mainly solves the problem of inaccurate SAR image segmentation in the prior art. The method is implemented by the steps of (1) performing SAR image sketching to obtain a sketch; (2) according to a regional chart of an SAR image, dividing pixel sub-spaces of the SAR image; (3) constructing a curvelet filter set; (4) building a convolutional structure learning model; (5) by adopting a curvelet filter and convolutional structure learning model-based SAR image segmentation method, segmenting pixel sub-spaces of a ground object adopting a mixed aggregation structure; (6) performing sketch line collection feature-based independent target segmentation; (7) performing visual semantic rule-based line target segmentation; (8) segmenting pixel sub-spaces of a homogeneous region by adopting a polynomial-based logic regression priori model; and (9) combining segmentation results to obtain an SAR image segmentation result. According to the method, good segmentation result of the SAR image is obtained; and the 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 curve wave filter and convolution structure learning in the technical field of image segmentation. The invention can accurately segment regions with different characteristics in 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) has been widely used in military and civilian fields due to its special imaging mechanism. SAR has the advantages of all-day, all-weather, multi-band, multi-polarization, variable side angle of view, and high resolution. It can not only map terrain and landforms in detail and accurately, and obtain information on the earth's surface. Vegetation collects subsurface information, providing detailed surface mapping data an...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that the high-level semantic knowledge of the SAR image is not introduced, and the SAR image is only segmented at the pixel level, resulting in inaccurate SAR image segmentation results.

The disadvantage of this method is that when obtaining the feature vector of the SAR image, it does not learn the unique structural features of the SAR image due to the correlation between pixels

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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