SAR image segmentation method based on ridgelet deconvolution network and sparse classification

Abstract

The invention discloses a SAR image segmentation method based on a ridgelet deconvolution network and sparse classification and mainly solves a problem that image features are extracted in virtue of human experience in the prior art. The method comprises following steps of: (1) sketching a synthetic aperture radar (SAR) image, (2) dividing the SAR image into different semantic regions; (3) training a ridgelet deconvolution network (RDN) of aggregation regions and a RDN of homogeneous regions; (4) merging similar aggregation regions; (5) merging similar homogeneous regions; (6) based on a watershed method, segmenting a structural region acquired in the step (2), and (7) acquiring the segmented SAR image. The SAR image segmentation method achieves good region consistency, improves the segmentation effect of the SAR image segmentation, and can be used for target detection and identification.

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 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

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