An upper-bound metric for characterizing spectral and spatial coregistration errors in spectral imaging.

Area of Science:

• Optics
• Remote Sensing
• Image Processing

Background:

• Coregistration errors in multi- and hyperspectral imaging sensors stem from differing spatial sensitivity patterns or spectral response variations across the field of view.
• Existing metrics like "smile" and "keystone" distortions do not capture errors from point spread function shape or spectral bandwidth variations.
• Accurate coregistration is crucial for reliable image data in spectral imaging applications.

Purpose of the Study:

• To propose improved metrics for quantifying spatial and spectral coregistration errors in imaging spectrometers.
• To establish metrics that provide an upper bound on data errors for hyperspectral imaging sensors.
• To facilitate the use of these metrics in optical design optimization and sensor benchmarking.

Main Methods:

• Developed novel metrics based on the integrated difference between point spread functions (PSFs) for both spatial and spectral dimensions.
• The proposed metrics are designed to account for variations in PSF shape and spectral bandwidth, which are not covered by traditional "smile" and "keystone" measures.
• Theoretical analysis demonstrated that the proposed metrics represent an upper bound on the coregistration error in recorded image data.

Main Results:

• The proposed metrics effectively quantify coregistration errors in hyperspectral imaging sensors, considering PSF variations.
• These metrics provide a quantifiable upper bound for data errors, offering a more comprehensive assessment than existing methods.
• The developed metrics are shown to be applicable for estimating actual data errors in specific image acquisitions.

Conclusions:

• The novel integrated difference metrics offer a significant advancement in assessing coregistration accuracy for spectral imaging sensors.
• These metrics enhance the reliability of hyperspectral data by providing a more robust error estimation.
• The proposed metrics are valuable tools for optical system design and for establishing performance benchmarks for spectral image sensors.