SAR image segmentation method based on feature learning and sketch line constraint

Abstract

The invention discloses an SAR image segmentation method based on feature learning and sketch line constraint, 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; (2), according to an area chart of the SAR image, dividing a pixel subspace of the SAR image; (3), performing feature learning by adopting a deconvolution model; (4), constructing a direction feature vector and a length feature vector, and performing filter structure clustering; (5), performing codebook projection based on direction constraint; (6), dividing a hybrid aggregation structured surface feature pixel subspace of the SAR image; (7), performing independent target segmentation based on the sketch line aggregation feature; (8), performing line target segmentation based on a visual semantic rule; (9), performing segmentation of a pixel subspace in a homogeneous area by adopting a polynomial-based logistic regression prior model; and (10), combining 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 (Synthetic Aperture Radar, SAR) image segmentation method based on ridgelet deconvolution network and sparse classification in the technical field of target recognition. The invention can accurately segment different regions 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 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 chang...

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 method that requires professional knowledge. Can good features be selected? To a large extent, 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 when comparing and reasoning the structural features of the disconnected regions in the aggregation region, the reasoning network used in this method is the self-organizing feature map SOM network, because the self-organizing map SOM itself has artificial Determining the number of clusters, the shortcomings of long clustering time, and SOM will cluster the filter features with obvious direction differences into one class when processing the SAR filter features, which will greatly reduce the clustering accuracy and greatly affect The accuracy of SAR image segmentation

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