2025
Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems

Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems

Adaptive Statistical Quality Assessment and Enhancement of Infrared and Optical Remote Sensing Imagery for Intelligent Environmental Monitoring in Real?Time

Published 2025-12-03

  • Rami Baazeem

Rami Baazeem
Department of MIS, University of Jeddah. Jeddah, Makkah, Saudi Arabia

Abstract

Infrared and optical remote sensing imagery plays a critical role in environmental applications such as wildfire detection, urban thermal profiling, climate monitoring, and sustainable resource management. Nevertheless, sensor noise, low contrast, motion blur, and atmospheric clutter usually affect the utility of such data, reducing its usefulness in real-time environmental monitoring and intelligent visual analytics. To address these challenges, the paper suggests a new adaptive statistical quality profiling and refinement architecture with future functionality in real time work of infrared and optical satellite images. The proposed framework incorporates the modeling of the temporal-spatial consistency with adaptive histogram-based statistical assessments and edge-preserving denoising techniques to reconstruct degraded scenes and deliver high-fidelity outcomes. The system can be used as a diagnostic layer in intelligent visual analytics pipelines as well as an enhancement module by incorporating both perceptual quality measures and statistical measures. Experiments on Sentinel and Landsat images indicate that the quantitative metrics are significantly enhanced: PSNR was improved to 29.8 dB and 32.3 dB when using an IR data and 24.1 dB and 26.5 dB when using an RGB optical data respectively. Similarly, SSIM, BRISque and NIQE increased by over 30 meaning that there was an improvement in perceptions. The framework not only is rapid in image fidelity, but also satisfies near real time performance, and can sustain throughput of up to 25 fps with GPU-accelerated inference and maintains the mean latency low, at less than 50 ms. These findings support the scalability of the given approach to the edge deployment within the process of satellite and ground-station. In addition to technical efficiency, the enhancements have a direct positive impact on environmental monitoring, making it possible to track the location of the hotspots of wildfires much better, haze less effectively to analyze air quality, and profile urban heat islands. In general, the research paper reflects a viable and scalable quality diagnostics and improvement system that facilitates sustainable environmental monitoring, smart visual analytics, and real-time Earth observation decision-making.

Keywords

Infrared image enhancement, optical remote sensing, real-time image processing, statistical quality assessment, intelligent visual analytics, environmental monitoring

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