Title

Assessment of Residual Nonuniformity on Hyperspectral Target Detection Performance

Date of Award

1-1-2019

Degree Name

M.S. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Bradley Ratliff

Abstract

Hyperspectral imaging sensors su?er from pixel-to-pixel response nonuniformity that manifests as ?xed pattern noise (FPN) in collected data. FPN is typically removed by application of ?at-?eld calibration procedures and nonuniformity correction algorithms. Despite application of these techniques, some amount of residual ?xed pattern noise (RFPN) may persist in the data, negatively impacting target detection performance. In this work we examine the conditions under which RFPN can impact detection performance using data collected in the SWIR across a range of target materials. We designed and conducted a unique tower-based experiment where we carefully selected target materials that have varying degrees of separability from natural grass backgrounds. Furthermore, we designed specially-shaped targets for this experiment that introduce controlled levels of mixing be tween the target and background materials to support generation of high ?delity receiver operating characteristic (ROC) curves in our detection analysis. We perform several studies using this collected data. First, we assess the detection performance after a conventional nonuniformity correction. We then apply several scene-based nonuniformity correction (SBNUC) algorithms from the literature and assess their abilities to improve target detection performance as a function of material separability. Then, we introduced controlled RFPN and study its adverse a?ects on target detection performance as well as the SBNUC techniques' ability to remove it. We demonstrate how residual ?xed pattern noise a?ects the detectability of each target class di?erently based upon its inherent separability from the background. A moderate inherently separable material from the background is a?ected the most by the inclusion of SBNUC algorithms.

Keywords

Electrical Engineering, Hyperspectral Imagery, Focal Plane Array, Residual Fixed Pattern Noise, Scene Based Nonuniformity Correction Algorithms, Receiver Operating Characteristic Curve

Rights Statement

Copyright 2019, author

Share

COinS