Target detection in FLIR imagery using independent component analysis

In this paper, we propose a target detection algorithm in FLIR imagery using independent component analysis (ICA). Here FLIR images of some real targets with practical background regions are used for training. Dimension of the training regions is chosen depending on the size of the target. After performing ICA transformation on these training images, we obtain a ICA matrix, where each row gives the transformed version of the previous matrix, and a weight matrix. Using these matrices, a transformed matrix of the input image can be found with enhanced features. Then cosine of the angle between the training and test vectors is employed as the parameter for detecting the unknown target. A test region is selected from the first frame of FLIR image, which is of the same size as the training region. This region is transformed following the proposed algorithm and then the cosine value is measured between this transformed vector and the corresponding vector of the transformed training matrix. Next the test region is shifted by one pixel and the same transformation and measurement are done. Thus the whole input frame is scanned and we get a matrix for cosine values. Finally a target is detected in a region of the input frame where it gives the highest cosine value. A detailed computer simulation program is developed for the proposed algorithm and a satisfactory performance is observed when tested with real FLIR images.