Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in
the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted
on a surveillance aircraft or unmanned aerial vehicle is used to capture the images of the forests with possible
hidden targets, e.g., rocket launchers. We consider random forests of longitudinal and latitudinal correlations.
Specifically, foliage coverage is encoded with a binary representation (i.e., foliage or no foliage), and is correlated in
adjacent regions. We address the detection problem of camouflaged targets hidden in random forests by building
memory into the observations. In particular, we propose an efficient algorithm to generate random forests,
ground, and camouflage of hidden targets with two dimensional correlations. The observations are a sequence
of snapshots consisting of foliage-obscured ground or target. Theoretically, detection is possible because there
are subtle differences in the correlations of the ground and camouflage of the rocket launcher. However, these
differences are well beyond human perception. To detect the presence of hidden targets automatically, we develop
a Markov representation for these sequences and modify the classical filtering equations to allow the Markov
chain observation. Particle filters are used to estimate the position of the targets in combination with a novel
random weighting technique. Furthermore, we give positive proof-of-concept simulations.