Advanced Optimization of Satellite Image Observation Matrices via Orthogonal Trigonometric Decomposition and Karhunen-Loeve Transform
文献类型：期刊论文
| 作者 | Z. S. Zhang, S. Shao, H. Y. Lv, H. L. Liu, D. Xie and T. Zhang |
| 刊名 | Traitement Du Signal
 |
| 出版日期 | 2023 |
| 卷号 | 40期号:3页码:895-904 |
| ISSN号 | 0765-0019 |
| DOI | 10.18280/ts.400306 |
| 英文摘要 | Satellite imagery, known for its high resolution and abundant informational content, presents unique opportunities for observation and reconstruction via compressed sensing. Despite the potential, inherent limitations in current compressed sensing observation matrices pose substantial challenges, primarily attributed to pronounced random fluctuations and inadequate robustness. Moreover, these matrices remain unsuccessful in eliminating spectral correlations. To mitigate these challenges, an innovative approach, rooted in Orthogonal Trigonometric Decomposition and Karhunen-Loeve Transform, is proposed, hereafter referred to as QRKL. This method demonstrates a marked improvement in optimizing the observation matrix, which is pivotal in compressed sensing specifically in the context of satellite image observation and reconstruction. Experimentally, when applied as the observation matrix, the QRKL transform matrix was observed to significantly enhance the reconstruction quality, stability, and anti-interference capabilities of satellite images. These improvements were noticeably superior compared to those achieved with standard observation matrices such as Gaussian and Bernoulli matrices. Furthermore, the utility of the QRKL optimization method extends beyond specific matrices, demonstrating a broad applicability to traditional observation matrices. This universal application implies that the QRKL method could potentially revolutionize compressed sensing practices in satellite imagery, leading to improved image reconstruction quality. The compelling results of this investigation suggest that QRKL transform-based optimization could provide a novel and powerful tool for advancing satellite imagery compressed sensing methodologies, thereby pushing the boundaries of the current state of the art. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/68220]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | Z. S. Zhang, S. Shao, H. Y. Lv, H. L. Liu, D. Xie and T. Zhang. Advanced Optimization of Satellite Image Observation Matrices via Orthogonal Trigonometric Decomposition and Karhunen-Loeve Transform[J]. Traitement Du Signal,2023,40(3):895-904. |
| APA | Z. S. Zhang, S. Shao, H. Y. Lv, H. L. Liu, D. Xie and T. Zhang.(2023).Advanced Optimization of Satellite Image Observation Matrices via Orthogonal Trigonometric Decomposition and Karhunen-Loeve Transform.Traitement Du Signal,40(3),895-904. |
| MLA | Z. S. Zhang, S. Shao, H. Y. Lv, H. L. Liu, D. Xie and T. Zhang."Advanced Optimization of Satellite Image Observation Matrices via Orthogonal Trigonometric Decomposition and Karhunen-Loeve Transform".Traitement Du Signal 40.3(2023):895-904. |
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