Detection Of Small Infrared Target Based On Background Prediction

The detection of small infrared target is the key problem in areas such as early warning, search and tracking, and infrared automatic homing guidance. How to improve the reliability and accuracy of small target detection has much research significance in infrared detection imaging. On the basis of introducing domestic and foreign development of small target detection, this paper studies the methods of small infrared target detection based on background prediction. The main tasks are as follows:Firstly, two detection methods of small infrared target are studied. One is based on dual tree complex wavelet transform (DT-CWT) and support vector regression (SVR). And the other is based on fuzzy least squares support vector machines (FLS-SVM). The former method suppresses the noise in infrared image by DT-CWT and predicts the background by SVR. And the latter one adopts FLS-SVM to predict the background of infrared image. The experimental results show that both methods can detect the small infrared target accurately and have high detection probability. Their detection results are better than the existing methods of small infrared target detection.Secondly, a detection method of small target in infrared image using nonsubsampled contourlet transform (NSCT), kernel fuzzy C means (KFCM) clustering and multi model LS-SVM is proposed. First, the infrared image is de-noised by NSCT. Then, the multi model LS-SVM based on KFCM clustering is adopted to predict the background of the de-noised infrared image. Finally, the real small taget is detected by segmenting the residual image and using the motion characteristics of small target. The results show that this method has higher detection probability and gain of signal-to-noise ratio (GSNR).Thirdly, a detection method of small infrared target is proposed, which is based on chaotic particle swarm optimization (CPSO) and spartial-temporal background prediction by least absolute deviation. First, a model of spatial-temporal background prediction is built. According to properties of least absolute deviation, the extreme value in least absolute deviation is selected by CPSO. The background in infrared image is predicted by this model and the small target is detected by segmenting the residual image. The results show this method is superior to the method of small infrared target detection based on background predication by least squares.Fourthly, a detection method of small infrared target based on gray prediction is realized. The GM(1.1) model of gray system theorey is adopted to predict the infrared image background in time domain. The small target can be detected by segmenting the residual image. The experimental results show that this method can achieve long-range small target detection.Fifthly, some threshold segmentation algorithms for the small infrared target of residual image are studied, such as the threshold selection method based on fuzzy Tsallis-Havrda-Charvat’s entropy, the threshold selection method based on recursive maximum between-cluster absolute difference and a two-dimensional histogram oblique segmentation method based on CPSO and fuzzy maximum entropy. The results show that the threshold selected by these methods can accurately segment the small target from infrared residual image.