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In this paper, we investigated automatic target detection and classification of low and high contrast targets present in unknown forward looking infrared (FLIR) image sequence. The detection algorithm, based on morphology based preprocessing, acts as a prescreener that selects possible candidate target regions, comprising both true targets and false alarms and places expected target-sized marker to those preselected regions. The application of simple non-linear grayscale operations in the proposed detection algorithm leads to real-time implementations. By considering the known target and background specific attributes, extracted from the training samples, the clutter rejection module discriminates between true target and false alarms previously identified by the detection algorithm. Two approaches are employed for object classification where one uses local features of the image and the other uses template matching technique such as image correlation. For the first approach, to extract features, we employed two methods - nonlinear filtering for texture energy measurement and wavelet decomposition by expending Daubechies high and low pass filter coefficients. Then for classification, a neural network based classifier is used. In the second approach minimax distance transform correlation filter (MDTCF) is applied that minimizes the average squared distance from the filtered true-class training images to a filtered reference image while maximizing the mean squared distance (MSD) of the filtered false-class training images to this filtered reference image. Then classification is performed using the squared distance of a filtered test image to the chosen filtered reference image. The performance of the proposed technique is analyzed for i) neural network with nonlinear texture filtering, ii) neural network with wavelet decomposition and iii) correlation filtering. Preliminary results indicate that the proposed detection algorithms can locate both hot and cold targets from cluttered background. In addition, the clutter rejecters are capable of maintaining a low false alarm rate and excellent discrimination competence. The performance of the proposed techniques has been tested with real life FLIR imagery supplied by the Army Missile Command (AMCOM).
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