Abstract
Due to the imaging mechanism of Synthetic Aperture Radars (SARs), the target shape on an SAR image is sensitive to the radar incidence angle and target azimuth, but there is strong correlation and redundancy between adjacent azimuth images of SAR targets. This paper studies multi-angle SAR image reconstruction based on non-negative Tucker decomposition using adjacent azimuth images reconstructed to form a sparse tensor. Sparse tensors are used to perform non-negative Tucker decomposition, resulting in non-negative core tensors and factor matrices. The reconstruction tensor is obtained by calculating the n-mode product of the core tensor and the factor matrix, and then image reconstruction is realized. The similarity between the original image and the reconstructed image is calculated by using the structural similarity index and the cosine of the angle between the feature vectors. The reconstruction results of three target images of MSTAR show that the reconstructed image has a similarity higher than 95% with the original image in most cases, which can support target recognition under sparse observation to a certain extent.
Funder
Natural Science Foundation of Hunan province, China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference19 articles.
1. A cascaded three-look network for aircraft detection in SAR images
2. Deep residual learning for image recognition;He;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016
3. Deepid3: Face recognition with very deep neural networks;Sun;arXiv,2015
4. Inception-v4, inception-resnet and the impact of residual connections on learning;Szegedy;Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (2017),2017
5. Very deep convolutional networks for large-scale image recognition;Simonyan;arXiv,2014
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