Infrared small target detection based on isotropic constraint under complex background

TL;DR

This paper introduces an isotropic constraint-based multilayer gray difference method for infrared small target detection in complex backgrounds, improving accuracy by suppressing non-isotropic regions.

Contribution

It proposes a novel multilayer gray difference approach combined with isotropic constraints to enhance small target detection in infrared images.

Findings

Method outperforms existing techniques in SCRG.

Achieves better ROC curve results.

Effective in complex background scenarios.

Abstract

Infrared search and tracking (IRST) system has been widely concerned and applied in the area of national defence. Small target detection under complex background is a very challenging task in the development of system algorithm. Low signal-to-clutter ratio (SCR) of target and the interference caused by irregular background clutter make it difficult to get an accurate result. In this paper, small targets are considered to have two characteristics of high contrast and isotropy, and we propose a multilayer gray difference (MGD) method constrained by isotropy. Firstly, the suspected regions are obtained through MGD, and then the eigenvalues of the original image's Hessian matrix are calculated to obtain the isotropy parameter of each region. Finally, those regions do not meet the isotropic constraint condition are suppressed. Experiments show that the proposed method is effective and superior to several common methods in terms of signal-to-clutter ratio gain (SCRG) and receiver operating characteristic (ROC) curve.