We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Single-Image Super-Resolution Method for Rotating Synthetic Aperture System Using Masking Mechanism.
- Authors
Sun, Yu; Zhi, Xiyang; Jiang, Shikai; Shi, Tianjun; Song, Jiachun; Yang, Jiawei; Wang, Shengao; Zhang, Wei
- Abstract
The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system.
- Subjects
SYNTHETIC apertures; REMOTE sensing; TRANSFORMER models; TECHNOLOGICAL innovations; SYNTHETIC aperture radar; OPTICAL remote sensing; DIGITAL computer simulation; MIRROR images
- Publication
Remote Sensing, 2024, Vol 16, Issue 9, p1508
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs16091508