Single-Image Super-Resolution Method for Rotating Synthetic Aperture System Using Masking Mechanism
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Published:2024-04-25
Issue:9
Volume:16
Page:1508
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Sun Yu1ORCID, Zhi Xiyang1ORCID, Jiang Shikai1, Shi Tianjun1ORCID, Song Jiachun1, Yang Jiawei1, Wang Shengao2, Zhang Wei1
Affiliation:
1. Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China 2. Division of System Engineering, Boston University, Boston, MA 02215, USA
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.
Funder
National Natural Science Foundation of China China Postdoctoral Science Foundation
Reference45 articles.
1. Yang, X., Li, F., Xin, L., Lu, X., Lu, M., and Zhang, N. (2020). An improved mapping with super-resolved multispectral images for geostationary satellites. Remote Sens., 12. 2. Yao, L., Liu, Y., and He, Y. (2018). A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images. Sensors, 18. 3. Pixel Level Fusion Techniques for SAR and Optical Images: A Review;Kulkarni;Inf. Fusion,2020 4. Yu, W., You, H., Lv, P., Hu, Y., and Han, B. (2021). A Moving Ship Detection and Tracking Method Based on Optical Remote Sensing Images from the Geostationary Satellite. Sensors, 21. 5. FFCA-YOLO for Small Object Detection in Remote Sensing Images;Zhang;IEEE Trans. Geosci. Remote Sens.,2024
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