Advancements in Image Resolution: Super-Resolution Algorithm for Enhanced EOS-06 OCM-3 Data

TL;DR

This paper presents SOCM-3, a super-resolution algorithm that significantly improves the spatial resolution of Ocean Color Monitor-3 satellite data, enhancing remote sensing applications across environmental and oceanic domains.

Contribution

The paper introduces a novel super-resolution model for OCM-3 data that effectively enhances image quality and resolution, addressing trade-offs in satellite imaging.

Findings

SOCM-3 improves image resolution below 240m in LAC mode and below 750m in GAC mode.

Evaluation shows SOCM-3 outperforms traditional methods in visual and computational quality metrics.

Enhanced resolution aids in better understanding of oceanic and environmental phenomena.

Abstract

The Ocean Color Monitor-3 (OCM-3) sensor is instrumental in Earth observation, achieving a critical balance between high-resolution imaging and broad coverage. This paper explores innovative imaging methods employed in OCM-3 and the transformative potential of super-resolution techniques to enhance image quality. The super-resolution model for OCM-3 (SOCM-3) addresses the challenges of contemporary satellite imaging by effectively navigating the trade-off between image clarity and swath width. With resolutions below 240 meters in Local Area Coverage (LAC) mode and below 750 meters in Global Area Coverage (GAC) mode, coupled with a wide 1550-kilometer swath and a 2-day revisit time, SOCM-3 emerges as a leading asset in remote sensing. The paper details the intricate interplay of atmospheric, motion, optical, and detector effects that impact image quality, emphasizing the necessity for advanced computational techniques and sophisticated algorithms for effective image reconstruction. Evaluation methods are thoroughly discussed, incorporating visual assessments using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metric and computational metrics such as Line Spread Function (LSF), Full Width at Half Maximum (FWHM), and Super-Resolution (SR) ratio. Additionally, statistical analyses, including power spectrum evaluations and target-wise spectral signatures, are employed to gauge the efficacy of super-resolution techniques. By enhancing both spatial resolution and revisit frequency, this study highlights significant advancements in remote sensing capabilities, providing valuable insights for applications across cryospheric, vegetation, oceanic, coastal, and domains. Ultimately, the findings underscore the potential of SOCM-3 to contribute meaningfully to our understanding of finescale oceanic phenomena and environmental monitoring.