SAR Image Segmentation Method Based on Deconvolution Network and Adaptive Inference Network

Abstract

The invention discloses a deconvolutional network and adaptive inference network based SAR image segmentation method and mainly solves the problem that human experience is required to extract image features in the prior art. The method is implemented by the steps of (1) sketching an SAR image; (2) extracting a complemented region graph of the SAR image; (3) training a deconvolutional network DNN for aggregation regions and homogeneous regions separately; (4) performing adaptive comparison inference on structural features of non-communicated regions in the aggregation and homogeneous regions to obtain segmentation results of the aggregation and homogeneous regions; (5) based on a watershed method, segmenting a structural region obtained in the step (2); and (6) combining the aggregation regions, the homogeneous regions and the structural region to obtain a segmentation result. According to the method, the segmentation result has relatively good regional consistency and the segmentation effect of the SAR image is improved. The method 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 SAR image segmentation method, which can be used for target detection or image recognition. Background technique [0002] SAR image segmentation refers to dividing the synthetic aperture radar SAR image into several disjoint regions according to the characteristics of grayscale, texture, structure and aggregation, and making these characteristics similar in the same region, but different in different regions. There are significant differences between 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 subsequent analysis and recognition. In general, the more accurate the segmentation,...

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 designed manually, and manually selecting features is a very laborious and professional-knowledge method. Whether good features can be selected is very important. To a certain 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 stacked denoising autoencoder used to automatically extract image features does not pay attention to the spatial relationship between pixels in the image, so it cannot better learn the structural features of the image and reduces the accuracy of SAR image segmentation. the accuracy of

Images