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Visual surveillance has been an active research area in computer vision and image processing, due to its crucial role in helping military intelligence and law enforcement agencies to fight against crime and terrorist activities. The goal of a visual surveillance system is to detect abnormal object behaviors and to raise alarms when such behaviors are detected. After moving objects are detected, it is essential to classify them into predefined categories, so that their motion behaviors can be appropriately interpreted in the context of their identities and their interactions with the environment. Consequently, object classification is a vital component in a complete visual surveillance system. RADAR (Radio Detection and Ranging) images are often undesirable in military applications because they reveal the location of the imaging system. So we explore visible and infrared images of ships which are generally more consistent than RADAR images and for which it is easier to compensate for environmental effects. Recent advances in visible and IR imaging technology improves its ability to observe objects at very long distances, but it is still militarily desirable to stay away as far as possible from potential enemy ships, which may be observed at different viewing angles. Several methods have been described in literature to perform the automatic classification of big ships (e.g. aircraftcarriers, combat ships, transportation ships, etc) from images, but none of them are based on small boat classification. A Principal Component Analysis (PCA) based target classification system is implemented for visible and IR small boat images which are captured in a highly cluttered background environment. Two robust segmentation algorithms were implemented, Graph-cut for grayscale IR small boat images, and Adaptive Progressive Thresholding (APT) for visible small boat images. These algorithms are tested for images captured in a highly cluttered background.