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24 August 1999 Small-target classification in ladar images with fuzzy templates
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This paper presents a method for image understanding that combines a fuzzy pixel-based feature extractor with a novel, multiple prototype classifier to detect and interpret small targets in LADAR intensity images when very few pixels on target are available. The method is based on the fuzzy c-means clustering algorithm (FCM). Prototypes are appended to an unprocessed image and low-level attributes of each pixel in the combined image are computed from the 8-neighbor pixels using FCM in 9 dimensions with 5 classes. A feature vector is then extracted from each prototype using a centered n x m window. The class membership vectors of the labeled prototypes are compared to the resulting class membership vectors of each unlabeled pixel to generate a set of confidences of a pixel's membership in the prototype classes. The fuzzy partition produced by FCM retains spatial integrity of each pixel label vector and relates the pixel level information contained in the partition to pixels in the data to be labeled. The method exhibits good behavior for images that do not contain any of the original prototype targets.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tim Hutcheson, Melanie A. Sutton, and James C. Bezdek "Small-target classification in ladar images with fuzzy templates", Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999);

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