SAR Image Segmentation Method Based on Structural Learning and Sketch Feature Inference Network

Abstract

The invention discloses an SAR image segmentation method based on structure learning and a sketch characteristic inference network and mainly aims to solve the problem that in the prior art, SAR image segmentation is not accurate. The method comprises the implementation steps that 1, a sketch is extracted according to a sketch model of an SAR image; 2, an area map is obtained according to the sketch of the SAR image, and the area map is mapped into the SAR image to obtain a mixed pixel subspace, a structure pixel subspace and a homogeneous pixel subspace of the SAR image; 3, feature learning is performed on the mixed pixel subspace; 4, the sketch characteristic inference network is constructed, and the mixed pixel subspace is segmented; 5, corresponding segmentation is performed on the structure pixel subspace and the homogeneous pixel subspace in sequence; and 6, segmentation results of all the pixel spaces are combined to obtain a final segmentation result. Through the method, the accuracy of SAR image segmentation is improved, and the method can be used for target detection and recognition in the SAR image of a synthetic aperture radar.

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, which can be used for target detection and recognition of subsequent synthetic aperture radar SAR images. Background technique [0002] Synthetic aperture radar SAR image segmentation refers to dividing the synthetic aperture radar SAR image into several mutually disjoint regions according to the characteristics of grayscale, texture, structure, aggregation, etc., and making these features appear similar in the same region, while Processes that show significant differences across regions. The purpose of synthetic aperture radar SAR image segmentation is to simplify or change the representation of the image, making the image easier to understand and analyze. Synthetic aperture radar SAR image segmentation is the basis of image understanding and interpretation, and the quality of segmentation directly affects...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the boundary positioning of the aggregated area is not accurate enough; the segmentation result of the homogeneous area is poorly consistent, and the number of categories is not reasonable; and the independent target is not processed in the segmentation result of the structural area

The disadvantage of this method is that the features used in the segmentation of synthetic aperture radar SAR images are manually extracted. Manually selecting features is a very laborious and requires professional knowledge. Whether good features can be selected is largely It depends on experience and luck, so the quality of artificially selected features often becomes the bottleneck of the entire system performance

The disadvantage of this method is that the input of the depth autoencoder used to automatically extract image features is a one-dimensional vector, which destroys the spatial structure features of the image. Therefore, the essential features of the image cannot be extracted, which reduces the efficiency of SAR image segmentation. precision

The disadvantage of this method is that in the process of feature learning, the sketch prior information in the image cannot be effectively used. The inference network used is the self-organizing feature map SOM network, which has the disadvantage of artificially determining the number of categories, and in terms of mapping, a single pixel feature map is used, and structural constraints are not added, resulting in the success of filter feature mapping with inconsistent directions. Greatly affects the accuracy of SAR image segmentation

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