Point target detection and subpixel position estimation in optical imagery

TL;DR

This paper improves point object detection and subpixel position estimation in optical imagery by addressing aliasing effects, proposing alternative detectors, and analyzing sensor design impacts.

Contribution

It introduces new detection methods considering aliasing effects and evaluates subpixel estimation techniques, highlighting sensor design influence.

Findings

Approximate and generalized likelihood ratio tests outperform pixel matched filtering.

Sensor design significantly affects detection and estimation performance.

Proposed methods enhance detection accuracy in cluttered backgrounds.

Abstract

This paper addresses the issue of detecting point objects in a clutter background and estimating their position by image processing. We are interested in the specific context where the object signature significantly varies with its random subpixel location because of aliasing. Conventional matched filter neglects this phenomenon and causes consistent loss of detection performance. Thus, alternative detectors are proposed and numerical results show the improvement brought by approximate and generalized likelihood ratio tests in comparison with pixel matched filtering. We also study the performance of two types of subpixel position estimators. Finally, we put forward the major influence of sensor design on both estimation and point object detection performance.