Target detection in cluttered FLIR imagery using probabilistic neural networks

Abstract

In this paper, we investigate the detection and classification of targets in forward-looking infrared (FLIR) imagery under various challenging scenarios. At first, morphological preprocessing is applied for the preliminary selection of all possible candidate target regions. Morphological operations decompose the given input image into a filtered image. Clutter rejection, i.e. the classification between desired target and background, is done by means of Probabilistic neural network (PNN). For most cases, only the samples of the desired target images are used for the training purposes, which are not adequate for cases, where the target is almost blended with the background. For instance, target like objects may be present in the region of interest (ROI) and there is very low contrast difference between target and background. Horizontal and vertical convolution with wavelet low pass filter coefficients serves to extract features for training the PNN. In this paper, an improved clutter rejecter is presented to overcome the inferior classification performance of alternate techniques for poorly centered targets by moving the marked candidate target window in suitable directions with respect to the center of the potential target patch to extract ROIs from each detected target region. Results are shown for introductory detection-classification, and on improved performance of the clutter rejecter, by considering several shifted ROIs to accurately classify the true target from the clutter. Test results confirm the excellent performance of the detector and the clutter rejecter when both target and background features are used for training, and several shifted ROIs are considered for precise classification of each ROI marked by the detector.