Optimal point target detection using adaptive auto regressive background prediction

Bradley S. Denney; Rui J. P. de Figueiredo

doi:10.1117/12.392005

13 July 2000 Optimal point target detection using adaptive auto regressive background prediction

Bradley S. Denney, Rui J. P. de Figueiredo

Proceedings Volume 4048, Signal and Data Processing of Small Targets 2000; (2000) https://doi.org/10.1117/12.392005
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Bradley S. Denney and Rui J. P. de Figueiredo "Optimal point target detection using adaptive auto regressive background prediction", Proc. SPIE 4048, Signal and Data Processing of Small Targets 2000, (13 July 2000); https://doi.org/10.1117/12.392005

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