Detecting targets occluded by foliage in foliage-penetrating (FOPEN) ultra-
wideband synthetic aperture radar (UWB SAR) images is an important and chal
lenging problem. Given the different nature of target returns in foliage an
d nonfoliage regions and very low signal-to-clutter ratio in UWB imagery, c
onventional detection algorithms fail to yield robust target detection resu
lts.
A new target detection algorithm is proposed that 1) incorporates symmetric
alpha-stable (S alpha S) distributions for accurate clutter modeling, 2) c
onstructs a two-dimensional (2-D) site model for deriving local context, an
d 3) exploits the site model for region-adaptive target detection. Theoreti
cal and empirical evidence is given to support the use of the S alpha S mod
el for image segmentation and constant false alarm rate (CFAR) detection. R
esults of our algorithm on real FOPEN images collected by the Army Research
Laboratory are provided.