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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 4
1261-1264

Super-resolution SAR reconstruction algorithm using stochastic gradient minimum variance pursuit with attributed scattering priors

Cong Xunchao1
Wan Qun2
1. The 10th Research Institution of China Electronics Technology Group Corporation, Chengdu 610036, China
2. School of Electronic Engineering, University of Electronic Science & Technology of China, Chengdu 611731, China
DOI: 10.19734/j.issn.1001-3695.2017.11.0101

Abstract

The applications in both the military and civil field have pressing needs and great expectations of achieving the target higher resolution and more detailed description. This paper firstly modeled the object-level SAR observations based on attributed scattering center(ASC) model in sparse representation framework. Secondly, it proposed a classifying strategy of the target attributes space for the object-level reconstruction in signal domain. Combined with data extrapolating, then it proposed a stochastic gradient minimum variance pursuit(SGMVP) based object-level super-resolution reconstruction algorithm. It finally achieved super-resolution image by FFT to effectively promotethe efficiency of the proposed algorithm. The proposed algorithm not only can achieve improved super-resolution image, but also provide accurate physically-relevant attributed features of the scatterers simultaneously. Experimental results confirm the effectiveness of the proposed algorithm.

Foundation Support

国家自然科学基金资助项目(U1533125)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.11.0101
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 4
Section: Technology of Graphic & Image
Pages: 1261-1264
Serial Number: 1001-3695(2019)04-067-1261-04

Publish History

[2019-04-05] Printed Article

Cite This Article

丛迅超, 万群. 基于属性散射信息的随机梯度最小方差追踪SAR超分辨重建算法 [J]. 计算机应用研究, 2019, 36 (4): 1261-1264. (Cong Xunchao, Wan Qun. Super-resolution SAR reconstruction algorithm using stochastic gradient minimum variance pursuit with attributed scattering priors [J]. Application Research of Computers, 2019, 36 (4): 1261-1264. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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