Small infrared target fusion detection based on support vector machines in the wavelet domain

A novel method for fusion detection of small infrared targets based on support vector machines (SVM) in the wavelet domain is presented. Target detection task plays an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. SVM is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation. Least-squares support vector machines (LS-SVMs) are reformulations to standard SVMs. The proposed algorithm can be divided into four steps. First, each frame of the image sequence is decomposed by the discrete wavelet frame (DWF). Second, the components with low frequency are performed by regression based on LS-SVM. The one-order partial derivatives in row and column directions are derived. Therefore, feature images of the gradient strength can be obtained. Third, feature images of five consecutive frames are fused to accumulate the energy of target of interest and greatly reduce false alarms. Finally, the segmentation method based on contrast between target and background is utilized to extract the target. In terms of connectivity of moving targets, the majority of residual clutter and false alarms that survive are removed based on 3-D morphological dilation across three consecutive frames along the motion direction of the moving targets. Actual infrared image sequences in backgrounds of sea and sky are applied to validate the proposed approach. Experimental results demonstrate the robustness of the proposed method with high performance.