Newsletter February 2025

A Deeper Look

A Newsletter of Innovative Imaging & Research Corporation

February 2025

Unlocking Image Quality: I2R’s Spatial Resolution Assessment Services

Welcome to the February 2025 edition of A Deeper Look. In this edition, we offer a glimpse of the important mathematical parameters that contribute to image quality, with examples that demonstrate their effects. I2R cut its teeth on quantitative image quality evaluations, filling a critical need to support advancing imaging systems.

Drawing on this expertise, I2R now provides Spatial Resolution Image Quality Estimation (SR-IQE) as a service, allowing customers to assess and validate their imaging system performance through secure I2R platforms. I2R contributed to developing the USGS Digital Imagery Guideline, which defines the key image quality metrics evaluated by SR-IQE. We also develop custom engineering targets. The image quality estimation technology can be used by imagery providers, users, buyers, and even sensor designers, while contributing to better science.

The Challenge

New satellite, aerial, and AI technologies exhibit increased spatial resolution performance. Without a standardized evaluation method, image quality assessments can be inconsistent, leading to misinterpretations of data and sensor capabilities.

To bridge this gap, I2R developed SR-IQE—an objective, automated approach to measuring spatial resolution across diverse imaging systems. But what exactly is spatial resolution, and why does it matter?

What is spatial resolution?

Spatial resolution plays a crucial role in determining the smallest detectable feature in an image. Key parameters that affect spatial resolution are the ground sample distance (GSD) and measures of image sharpness, including Modulation Transfer Function (MTF) and Relative Edge Response (RER).

The GSD defines pixel size on the ground.

Urban Image with GSD of 1 m

Urban Image with GSD of 0.5 m

Urban Image with GSD of 0.15 m

MTF and RER measure image sharpness or blur. MTF describes how contrast is preserved at different spatial frequencies, while RER measures how sharply an edge transitions between bright and dark regions. MTF and RER are estimated using images that contain natural edges or engineered edge targets.

Estimation of MTF and RER from an Imaged Edge

Here are example images measuring sharpness using MTF@Nyquist and RER. All of the images have the same GSD.

Urban Image with 10 m GSD and MTF@Nyquist Value of 0.03

Urban Image with 10 m GSD and MTF@Nyquist Value of 0.4

Urban Image with 10 m GSD and MTF@Nyquist Value of 0.8

River Image with 10 m GSD and Relative Edge Response Value of 0.45

River Image with 10 m GSD and Relative Edge Response Value of 0.58

River Image with 10 m GSD and Relative Edge Response Value of 0.97

These attributes – pixel size and image sharpness – can interact with each other in unintended ways, making image quality assessment and optimization difficult.

I2R’s Solution: SR-IQE as a Service

I2R’s SR-IQE service provides a quantitative, automated evaluation of spatial resolution, offering two key approaches:

Traditional methods that utilize engineered targets for controlled assessments
Statistical approaches that leverage naturally occurring edges within imagery, enabling assessments without requiring specialized test sites

Our SR-IQE service allows customers to securely upload their imagery for analysis, generating detailed reports on image sharpness, resolution performance, and quality consistency over time.

Why Choose SR-IQE?

Our service provides several critical benefits:

Data Specification Validation: Ensures that sensor performance aligns with stated capabilities
Image Utility Determination: Assesses whether an image meets mission-specific requirements
NIIRS Ratings: Estimates key parameters that contribute to image quality equations used in the National Imagery Interpretability Rating Scale (NIIRS) ratings, ensuring that assessments align with established standards for image interpretability
AI & Image Processing Integration: Supplies key parameters for AI-driven analytics and image restoration
Optical System Optimization: Supports long-term focus monitoring and system adjustments
Elimination of Physical Targets: Reduces cost and logistical challenges associated with designing, constructing and deploying engineered targets

Proven Performance

I2R’s SR-IQE methodology has been validated across multiple imaging platforms, demonstrating strong agreement with laboratory-based characterizations. Our automated approach has been tested on urban and agricultural landscapes, providing statistically robust spatial resolution metrics that support mission-critical applications.

Available Services

Automated SR-IQE analysis of pre-existing imagery, generating statistically robust assessments across an entire focal plane
Uncertainty estimation based on a comprehensive statistical framework
Trend analysis and focus tracking over extended operational periods
Custom design of engineered targets for controlled validation

Example Engineered Target Site at Baotou, China (Source: Google Earth)

As part of the SR-IQE analysis process, users receive detailed summary statistics and visual reports that provide a comprehensive view of spatial resolution performance. These reports include

MTF curves and edge response functions to quantify image sharpness
Spatial resolution trend analysis over multiple acquisitions
Uncertainty estimates based on a robust statistical framework

Example Results from I2R’s SR-IQE Analysis Using Natural Targets

Multiple Edges with Statistical Assessment Applied

Sample Summary Statistics Compiled by the SR-IQE

Whether you need to validate sensor specifications, optimize imaging performance, or integrate AI-driven analytics, SR-IQE provides a reliable and efficient solution.

Interested in improving your imaging system’s performance? Contact us at info@i2rcorp.com to learn how I2R’s SR-IQE service can provide precise, actionable insights for your mission.

A Deeper Read

Explore the USGS Digital Imagery Guideline
I2R’s JACIE presentations that incorporate spatial resolution estimation:
A comprehensive paper on engineering targets:
- Pagnutti, M., S. Blonski, M. Cramer, D. Helder, K. Holekamp, E. Honkavaara, & R. Ryan. 2010. “Targets, Methods, and Sites for Assessing the In-Flight Spatial Resolution of Electro-Optical Data Products.” Canadian Journal of Remote Sensing 36(5): 583-601.

Thank you for staying connected! Look for future updates as we continue to innovate in imaging technology.

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