Moving Dim and Small Target Detection in Multiframe Infrared Sequence With Low SCR Based on Temporal Profile Similarity
Xin Liu, Liyuan Li, Liqi Liu, Xiaofeng Su, Fansheng Chen
Abstract
Research about infrared dim and small target detection (DSTD) is concentrated on single-frame algorithms, which are limited by the contrast between target and background and face with the problems of low detection probability, high false alarm rate, and lack of robustness in low signal-to-clutter ratio (SCR) and strong noise environment. Studying the use of multiframe sequences adequately is necessary. In order to effectively utilize the temporal and local spatial information of infrared sequences, we propose a similarity model for pixel temporal profile (TP). Different from current TP detection methods, we study waveform similarity calculation for detection, and local time-shift characteristics to eliminate false alarms. First, fast Fourier transform (FFT) and KL divergence are applied to calculate the similarity of TP and reference waveform, and errors due to time offsets can be avoided through the frequency domain; second, the peak ratio of the FFT is applied to calculate the time shift of the neighboring pixels relative to the center pixel; third, maximum suppression strategy is used to reduce false alarms. Experiments show that the model and algorithms proposed in this letter have excellent performance in target enhancement and background suppression, and have higher performance than other methods in receiver operator characteristic curve (ROC).