English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day. To reduce the sampling amount of video SAR, image processing can be formulated into a low-tubal-rank tensor recovery problem. In this article, we proposed an orthogonal rank-1 tensor pursuit (ORTP) algorithm to solve the low-tubal-rank tensor recovery problem in video SAR imaging. The proposed ORTP algorithm is an extension of the orthogonal rank-1 matrix pursuit algorithm in the matrix sensing problem from the matrix case to the tensor case under a tubal-rank model. It is capable of reconstructing the target tensor efficiently without requiring any prior information about the prespecified or pre-estimated tensor tubal-rank value. To achieve this, rank-1 basis tensors and weight tensors of the target tensor are estimated iteratively, and the residual error between the observed tensor and the estimated tensor through linear mapping is utilized as the stop condition. We theoretically prove the convergence and correctness of the proposed ORTP method. The methodology was tested on synthetic data, real video data, and video SAR data. These tests show that the proposed approach outperforms other video SAR imaging algorithms and low-rank tensor recovery algorithms.

ORTP: A Video SAR Imaging Algorithm Based on Low-Tubal-Rank Tensor Recovery