LRT-DetSim: Long-Range Airborne Target Detection Simulator for Infra-Red Image Sequences

Early detection of airborne threats is becoming more and more important for advancing attack distance and response speed of modern hi-tech weapons. The targets at longer ranges appear on the sensor image plane as point and small targets with low contrast. Therefore, the study and implementation of low contrast point and small target detection algorithms for infra-red image sequences are important for modern hitech weapons. Typically, a spatial pre-processing step is performed on the input image to predict the background and consequently enhance the signal-to-clutter-ratio followed by actual detection algorithm. The proposed Long-Range Target Detection Simulator (LRT-DetSim) is a graphical user interface (GUI) based simulator for detection of point and small targets in infra-red image sequences. In the LRT-DetSim, we have implemented five pre-processing algorithms, namely max-median filter, two morphological filters, contour structuring element based (CB) top-hat transform and a CB modified top-hat transform, as well as two detection algorithms, the Modified Motion-Analysis (MMA) algorithm and the Adaptive Morphological Clutter Elimination (AMCE) algorithm. From the simulation, we have found that the CB top-hat transform based pre-processing algorithms outperform the other pre-processing algorithms in terms of Mean Absolute value of Residual Background (MARB) as well as Signal to Clutter Ratio (SCR). The MMA algorithm demonstrated higher probability of detection but generates many false alarms for highly clouded background. In contrast to this, the AMCE algorithm is more suitable for highly clouded clutter scenario.