Neural Operator for Planetary Remote Sensing Super-Resolution with Spectral Learning

Hui-Jia Zhao, Jie Lu, Wen-Xiu Guo and Xiao-Ping Lu ()
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Hui-Jia Zhao: School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau, China
Jie Lu: School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau, China
Wen-Xiu Guo: School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau, China
Xiao-Ping Lu: School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau, China

Mathematics, 2024, vol. 12, issue 22, 1-20

Abstract: High-resolution planetary remote sensing imagery provides detailed information for geomorphological and topographic analyses. However, acquiring such imagery is constrained by limited deep-space communication bandwidth and challenging imaging environments. Conventional super-resolution methods typically employ separate models for different scales, treating them as independent tasks. This approach limits deployment and real-time applications in planetary remote sensing. Moreover, capturing global context is crucial in planetary remote sensing images due to their contextual similarities. To address these limitations, we propose Discrete Cosine Transform (DCT)–Global Super Resolution Neural Operator (DG-SRNO), a global context-aware arbitrary-scale super-resolution model. DG-SRNO achieves super-resolution at any scale using a single framework by learning the mapping between low-resolution (LR) and high-resolution (HR) function spaces. We mathematically prove the global receptive field of DG-SRNO. To evaluate DG-SRNO’s performance in planetary remote sensing tasks, we introduce the Ceres 800 dataset, a planetary remote sensing super-resolution dataset. Extensive quantitative and qualitative experiments demonstrate DG-SRNO’s impressive reconstruction capabilities.

Keywords: deep learning; planetary remote sensing; neural operator; super resolution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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