Modern Defense Technology ›› 2024, Vol. 52 ›› Issue (1): 92-101.DOI: 10.3969/j.issn.1009-086x.2024.01.012

• TARGET CHARACTERISTIC, DETECTION AND TRACKING TECHNOLOGY • Previous Articles     Next Articles

Spectral Image Compressed Sensing Reconstruction Based on Tensor Decomposition

Ziyuan ZHAO1, Yidong TANG2, Shucai HUANG3   

  1. 1.College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
    2.PLA 95607 Troops, Chengdu 610066, China
    3.College of Air and Missile Defense, Air Force Engineering University, Xi’an 710051, China
  • Received:2022-11-30 Revised:2023-03-23 Online:2024-02-28 Published:2024-02-21
  • Contact: Yidong TANG

基于张量分解的光谱图像压缩感知重构

赵梓渊1, 唐意东2, 黄树彩3   

  1. 1.空军工程大学 信息与导航学院, 陕西 西安 710077
    2.中国人民解放军95607部队, 四川 成都 610066
    3.空军工程大学 防空反导学院, 陕西 西安 710051
  • 通讯作者: 唐意东
  • 作者简介:赵梓渊(2000-),男,山西永济人。硕士生,研究方向为图像处理与指挥信息系统。
  • 基金资助:
    国家自然科学基金(61703424)

Abstract:

The spectral imaging provides important support for ballistic missile early warning by virtue of its abundant spatial and spectral information, and the compressive sensing provides a effective approach for spectral image data collecting and processing. Aiming at the existing compressed perceptual reconstruction mostly adopts the coding method of "spatial domain compressed sampling and inter-spectral traditional compression", which still exists a certain waste of resources, a compressed perceptual reconstruction method based on tensor decomposition for spectral images is proposed. Taking use of the sparsity of spectral image data in three-dimensional space, a reconstruction model based on Tucker decomposition is built, and the solution algorithm based on orthogonal matching pursuit(OMP) is given. Moreover, an improved OMP algorithm which takes three-dimension tensors as dictionary atoms is proposed by expanding traditional OMP algorithm into three-dimensional space. The experimental results indicate that the proposed method can effectively reduce algorithm complexity and improve the performance of reconstruction.

Key words: spectral image, sparse mode, tensor decomposition, compressed sensing, OMP(orthogonal matching pursuit) algorithm

摘要:

光谱成像丰富的空间信息和光谱信息能够为弹道导弹预警探测提供重要的信息支撑,压缩感知则为实现光谱图像数据的高效采集和处理提供了有效途径。针对现有压缩感知重构多采用“空间域压缩采样和谱间传统压缩”的编码方式,仍存在一定资源浪费的问题,提出了一种基于张量分解的光谱图像压缩感知重构方法。该方法利用光谱图像数据的三维空间稀疏性,建立基于三阶张量Tucker分解的光谱图像重构模型,基于正交匹配算法设计相应的模型求解方法;将传统正交匹配算法推广到三维空间,设计一种以三阶张量为字典原子的正交匹配追踪算法,在三维空间实现光谱图像数据的压缩采样及解码重构。实验分析结果表明,该方法能够充分利用光谱图像三维数据块结构信息,有效降低重构算法复杂度,增强压缩感知重构算法性能。

关键词: 光谱图像, 稀疏模型, 张量分解, 压缩感知, OMP算法

CLC Number: