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Proceedings Paper

Optimal point target detection using adaptive auto regressive background prediction
Author(s): Bradley S. Denney; Rui J. P. de Figueiredo
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Paper Abstract

In this paper, we use an adaptive AR (Auto regressive) model to optimally filter background texture from images. The filter maps background texture into minimum variance locally white noise. Ad additive point target signal, however, is unaffected by the filter thereby effectively maximizing the signal to noise/clutter ratio. Thus, filter output thresholding can detect anomalous pixels in the images. Additionally the paper introduces a false alarm rejection scheme based on the intersection of a Four Quadrant AR (Quad-AR) filter. The paper addresses the implicit background assumptions in this approach and the median filter approach to small target detection. An example application of the filter to infrared images of missiles immersed in intense sea glint is presented. The AR filter performance is compared to a median filter performance. It is shown that for the infrared sub-pixel missile over sea problem, the Quad-AR approach is substantially better than previous approaches.

Paper Details

Date Published: 13 July 2000
PDF: 12 pages
Proc. SPIE 4048, Signal and Data Processing of Small Targets 2000, (13 July 2000); doi: 10.1117/12.392005
Show Author Affiliations
Bradley S. Denney, Neural Computing Systems (United States)
Rui J. P. de Figueiredo, Neural Computing Systems and Univ. of California/Irvine (United States)

Published in SPIE Proceedings Vol. 4048:
Signal and Data Processing of Small Targets 2000
Oliver E. Drummond, Editor(s)

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