D. Borghys et al., MULTILEVEL DATA FUSION FOR THE DETECTION OF TARGETS USING MULTISPECTRAL IMAGE SEQUENCES, Optical engineering, 37(2), 1998, pp. 477-484
An approach is presented to the long range automatic detection of vehi
cles, using multisensor image sequences, The method is tested on a dat
abase of multispectral image sequences, acquired under diverse operati
onal conditions, The approach consists of two parts, The first part us
es a semisupervised approach, based on texture parameters, for detecti
ng stationary targets, Far each type of sensor one learning image is c
hosen, Texture parameters are calculated at each pixel of the !earning
images and are combined using logistic regression into a value that r
epresents the conditional probability that the pixel belongs to a targ
et given the texture parameters, The actual detection algorithm applie
s the same combination to the texture features calculated on the remai
nder of the database (test images), When the results of this feature-l
evel fusion are stored as an image, the local maxima correspond to lik
ely target positions, These feature-level-fused images are calculated
for each sensor. In a sensor fusion step, the results obtained per sen
sor are then combined again, Region growing around the local maxima is
then used to detect the targets, The second part of the algorithm sea
rches for moving targets, To detect moving vehicles, any motion of the
sensor must be detected first, if sensor motion is detected, it is es
timated using a Markov random field model, Available prior knowledge a
bout the sensor motion is used to simplify the motion estimation. The
estimate is used to warp past images onto the current image in a tempo
ral fusion approach and moving targets are detected by thresholding th
e difference between the original and warped images. Decision level fu
sion combines the results from both parts of the algorithm, (C) 1998 S
ociety of Photo-Optical Instrumentation Engineers.