Adaptive scene-based correction algorithm for removal of residual fixed pattern noise in microgrid image data

Bradley M. Ratliff; Daniel A. LeMaster

doi:10.1117/12.916697

11 June 2012 Adaptive scene-based correction algorithm for removal of residual fixed pattern noise in microgrid image data

Bradley M. Ratliff, Daniel A. LeMaster

Author Affiliations +

Proceedings Volume 8364, Polarization: Measurement, Analysis, and Remote Sensing X; 83640N (2012) https://doi.org/10.1117/12.916697
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Pixel-to-pixel response nonuniformity is a common problem that affects nearly all focal plane array sensors. This results in a frame-to-frame fixed pattern noise (FPN) that causes an overall degradation in collected data. FPN is often compensated for through the use of blackbody calibration procedures; however, FPN is a particularly challenging problem because the detector responsivities drift relative to one another in time, requiring that the sensor be recalibrated periodically. The calibration process is obstructive to sensor operation and is therefore only performed at discrete intervals in time. Thus, any drift that occurs between calibrations (along with error in the calibration sources themselves) causes varying levels of residual calibration error to be present in the data at all times. Polarimetric microgrid sensors are particularly sensitive to FPN due to the spatial differencing involved in estimating the Stokes vector images. While many techniques exist in the literature to estimate FPN for conventional video sensors, few have been proposed to address the problem in microgrid imaging sensors. Here we present a scene-based nonuniformity correction technique for microgrid sensors that is able to reduce residual fixed pattern noise while preserving radiometry under a wide range of conditions. The algorithm requires a low number of temporal data samples to estimate the spatial nonuniformity and is computationally efficient. We demonstrate the algorithm's performance using real data from the AFRL PIRATE and University of Arizona LWIR microgrid sensors.

Citation Download Citation

Bradley M. Ratliff and Daniel A. LeMaster "Adaptive scene-based correction algorithm for removal of residual fixed pattern noise in microgrid image data", Proc. SPIE 8364, Polarization: Measurement, Analysis, and Remote Sensing X, 83640N (11 June 2012); https://doi.org/10.1117/12.916697

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available