Geometric Correction and Mosaic Generation of Geo High Resolution Camera Images

The Geo High Resolution Camera (GHRC) aboard ISRO GSAT-29 satellite is a state-of-the-art 6-band Visible and Near Infrared (VNIR) imager in geostationary orbit at 55degE longitude. It provides a ground sampling distance of 55 meters at nadir, covering 110x110 km at a time, and can image the entire Earth disk using a scan mirror mechanism. To cover India, GHRC uses a two-dimensional raster scanning technique, resulting in over 1,000 scenes that must be stitched into a seamless mosaic. This paper presents the geolocation model and examines potential sources of targeting error, with an assessment of location accuracy. Challenges in inter-band registration and inter-frame mosaicing are addressed through algorithms for geometric correction, band-to-band registration, and seamless mosaic generation. In-flight geometric calibration, including adjustments to the instrument interior alignment angles using ground reference images, has improved pointing and location accuracy. A backtracking algorithm has been developed to correct frame-to-frame mosaicing errors for large-scale mosaics, leveraging geometric models, image processing, and space resection techniques. These advancements now enable the operational generation of full India mosaics with 100-meter resolution and high geometric fidelity, enhancing the GHRC capabilities for Earth observation and monitoring applications.

Hyperspectral Spatial Super-Resolution using Keystone Error

Hyperspectral images enable precise identification of ground objects by capturing their spectral signatures with fine spectral resolution.While high spatial resolution further enhances this capability, increasing spatial resolution through hardware like larger telescopes is costly and inefficient. A more optimal solution is using ground processing techniques, such as hypersharpening, to merge high spectral and spatial resolution data. However, this method works best when datasets are captured under similar conditions, which is difficult when using data from different times. In this work, we propose a superresolution approach to enhance hyperspectral data's spatial resolution without auxiliary input. Our method estimates the high-resolution point spread function (PSF) using blind deconvolution and corrects for sampling-related blur using a model-based superresolution framework. This differs from previous approaches by not assuming a known highresolution blur. We also introduce an adaptive prior that improves performance compared to existing methods. Applied to the visible and near-infrared (VNIR) spectrometer of HySIS, ISRO hyperspectral sensor, our algorithm removes aliasing and boosts resolution by approximately 1.3 times. It is versatile and can be applied to similar systems.

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

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.

Data Processing Chain and Products of EOS-06 OCM-3 Payload From Signal Processing to Geometric Precision

The Ocean Color Monitor-3, launched aboard Oceansat-3, represents a significant advancement in ocean observation technology, building upon the capabilities of its predecessors. With thirteen spectral bands, OCM-3 enhances feature identification and atmospheric correction, enabling precise data collection from a sun-synchronous orbit. With thirteen spectral bands, OCM-3 enhances feature identification and atmospheric correction, enabling precise data collection from a sunsynchronous orbit. Operating at an altitude of 732.5 km, the satellite achieves high signal-to-noise ratios (SNR) through sophisticated onboard and ground processing techniques, including advanced geometric modeling for pixel registration.The OCM-3 processing pipeline, consisting of multiple levels, ensures rigorous calibration and correction of radiometric and geometric data. This paper presents key methodologies such as dark data modeling, photo response non-uniformity correction, and smear correction, are employed to enhance data quality. The effective implementation of ground time delay integration (TDI) allows for the refinement of SNR, with evaluations demonstrating that performance specifications were exceeded. Geometric calibration procedures, including band-to-band registration and geolocation accuracy assessments, which further optimize data reliability are presented in the paper. Advanced image registration techniques leveraging Ground Control Points (GCPs) and residual error analysis significantly reduce geolocation errors, achieving precision within specified thresholds. Overall, OCM-3 comprehensive calibration and processing strategies ensure high-quality, reliable data crucial for ocean monitoring and change detection applications, facilitating improved understanding of ocean dynamics and environmental changes.