Detection of Small Infrared Target Based on Spatial-Temporal Prediction by Chaotic PSO and Least Absolute Deviation

Considering the characteristics of background and small targets in infrared images,a detection method of small infrared targets is proposed,which is based on chaotic particle swarm optimization(PSO) and spatial-temporal background prediction by least absolute deviation.Firstly,a model of spatial-temporal background prediction is built.According to the properties of least absolute deviation,extreme values in the least absolute deviation are selected by chaotic PSO.The background in the infrared image is predicted and the predicted background image is subtracted from the source image to give a residual image.Then,a two-dimensional histogram oblique segmentation method based on chaotic PSO and fuzzy maximum entropy is presented.The small target is detected by thresholding the obtained residual image.The experimental results were compared with the results of small infrared target detection method based on background predication by least squares.The experimental results show that the proposed method has higher detection probability and provide better gain of signal-to-noise ratio(GSNR).The proposed method is superior to the method of small infrared target detection based on background predication by least squares.